Leveraging Unlabeled Data for Glioma Molecular Subtype and Survival Prediction

Nicholas Nuechterlein, Beibin Li, Mehmet Saygin Seyfioglu, Sachin Mehta, Patrick Cimino, Linda Shapiro
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.1

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Auto-TLDR; Multimodal Brain Tumor Segmentation Using Unlabeled MR Data and Genomic Data for Cancer Prediction

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In this paper, we address two long-standing challenges in neuro-oncology: (1) how to leverage large amounts of unlabeled magnetic resonance (MR) imaging data for radiogenomic tasks and (2) how to unite glioma MR imaging with genomic data. We examine multi-parametric MR data from 542 patients in the combined training, validation, and testing sets of the 2018 Multimodal Brain Tumor Segmentation Challenge and somatic copy number alteration (SCNA) data from 1090 patients in The Cancer Genome Archive's (TCGA) lower-grade glioma and glioblastoma projects. We propose a novel application of multi-task learning (MTL) that leverages unlabeled MR data by jointly learning tumor segmentation masks with glioma molecular subtype markers and allows for SCNA input when available. There are 235 patients in the intersection of these MR and SCNA datasets, which we divide into an unlabeled training set, a labeled training set, and a validation set. Our MTL model significantly outperforms comparable classification models trained only on labeled MR data for both IDH1/2 mutation and 1p/19q co-deletion glioma subtype marker prediction tasks. We also observe that models trained on genomic and imaging data improve survival prediction results achieved by models trained on either alone. We will release our source code for future research.

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Deep Learning-Based Type Identification of Volumetric MRI Sequences

Jean Pablo De Mello, Thiago Paixão, Rodrigo Berriel, Mauricio Reyes, Alberto F. De Souza, Claudine Badue, Thiago Oliveira-Santos
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 16:00 in session PS T5.8

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Auto-TLDR; Deep Learning for Brain MRI Sequences Identification Using Convolutional Neural Network

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The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical professionals to monitor the progression of a brain tumor. As the interest for automatizing brain volume MRI analysis increases, it becomes convenient to have each sequence well identified. However, the unstandardized naming of MRI sequences make their identification difficult for automated systems, as well as make it difficult for researches to generate or use datasets for machine learning research. In face of that, we propose a system for identifying types of brain MRI sequences based on deep learning. By training a Convolutional Neural Network (CNN) based on 18-layer ResNet architecture, our system is able to classify a volumetric brain MRI as a T1, T1c, T2 or FLAIR sequence, or whether it does not belong to any of these classes. The network was trained with both pre-processed (BraTS dataset) and non-pre-processed (TCGA-GBM dataset) images with diverse acquisition protocols, requiring only a few layers of the volume for training. Our system is able to classify among sequence types with an accuracy of 96.27%.

3D Medical Multi-Modal Segmentation Network Guided by Multi-Source Correlation Constraint

Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 16:00 in session PS T5.8

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Auto-TLDR; Multi-modality Segmentation with Correlation Constrained Network

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In the field of multimodal segmentation, the correlation between different modalities can be considered for improving the segmentation results. In this paper, we propose a multi-modality segmentation network with a correlation constraint. Our network includes N model-independent encoding paths with N image sources, a correlation constrain block, a feature fusion block, and a decoding path. The model-independent encoding path can capture modality-specific features from the N modalities. Since there exists a strong correlation between different modalities, we first propose a linear correlation block to learn the correlation between modalities, then a loss function is used to guide the network to learn the correlated features based on the correlation representation block. This block forces the network to learn the latent correlated features which are more relevant for segmentation. Considering that not all the features extracted from the encoders are useful for segmentation, we propose to use dual attention based fusion block to recalibrate the features along the modality and spatial paths, which can suppress less informative features and emphasize the useful ones. The fused feature representation is finally projected by the decoder to obtain the segmentation result. Our experiment results tested on BraTS-2018 dataset for brain tumor segmentation demonstrate the effectiveness of our proposed method.

A Multi-Task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation

Ngan Le, Kashu Yamazaki, Quach Kha Gia, Thanh-Dat Truong, Marios Savvides
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.1

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Auto-TLDR; Contextual Brain Tumor Segmentation Using 3D atrous Residual Networks and Cascaded Structures

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In recent years, deep neural networks have achieved state-of-the-art performance in a variety of recognition and segmentation tasks in medical imaging including brain tumor segmentation. We investigate that segmenting brain tumor is facing to the imbalanced data problem where the number of pixels belonging to background class (non tumor pixel) is much larger than the number of pixels belonging to foreground class (tumor pixel). To address this problem, we propose a multi-task network which is formed as a cascaded structure and designed to share the feature maps. Our model consists of two targets, i.e., (i) effectively differentiating brain tumor regions and (ii) estimating brain tumor masks. The first task is performed by our proposed contextual brain tumor detection network, which plays the role of an attention gate and focuses on the region around brain tumor only while ignore the background (non tumor area). Instead of processing every pixel, our contextual brain tumor detection network only processes contextual regions around ground-truth instances and this strategy helps to produce meaningful regions proposals. The second task is built upon a 3D atrous residual network and under an encode-decode network in order to effectively segment both large and small objects (brain tumor). Our 3D atrous residual network is designed with a skip connection to enables the gradient from the deep layers to be directly propagated to shallow layers, thus, features of different depths are preserved and used for refining each other. In order to incorporate larger contextual information in volume MRI data, our network is designed by 3D atrous convolution with various kernel sizes, which enlarges the receptive field of filters. Our proposed network has been evaluated on various datasets including BRATS2015, BRATS2017 and BRATS2018 datasets with both validation set and testing set. Our performance has been benchmarked by both region-based metrics and surface-based metrics. We also have conducted comparisons against state-of-the-art approaches.

SAGE: Sequential Attribute Generator for Analyzing Glioblastomas Using Limited Dataset

Padmaja Jonnalagedda, Brent Weinberg, Jason Allen, Taejin Min, Shiv Bhanu, Bir Bhanu
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 16:00 in session PS T1.12

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Auto-TLDR; SAGE: Generative Adversarial Networks for Imaging Biomarker Detection and Prediction

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While deep learning approaches have shown remarkable performance in many imaging tasks, most of these methods rely on availability of large quantities of data. Medical image data, however, is scarce and fragmented. Generative Adversarial Networks (GANs) have recently been very effective in handling such datasets by generating more data. If the datasets are very small, however, GANs cannot learn the data distribution properly, resulting in less diverse or low-quality results. One such limited dataset is that for the concurrent gain of 19/20 chromosomes (19/20 co-gain), a mutation with positive prognostic value in Glioblastomas (GBM). In this paper, we detect imaging biomarkers for the mutation to streamline the extensive and invasive prognosis pipeline. Since this mutation is relatively rare, i.e. small dataset, we propose a novel generative framework – the Sequential Attribute GEnerator (SAGE), that generates detailed tumor imaging features while learning from a limited dataset. Experiments show that not only does SAGE generate high quality tumors when compared to standard Deep Convolutional GAN (DC-GAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP), it also captures the imaging biomarkers accurately.

A Benchmark Dataset for Segmenting Liver, Vasculature and Lesions from Large-Scale Computed Tomography Data

Bo Wang, Zhengqing Xu, Wei Xu, Qingsen Yan, Liang Zhang, Zheng You
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 12:00 in session PS T5.5

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Auto-TLDR; The Biggest Treatment-Oriented Liver Cancer Dataset for Segmentation

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How to build a high-performance liver-related computer assisted diagnosis system is an open question of great interest. However, the performance of the state-of-art algorithm is always limited by the amount of data and quality of the label. To address this problem, we propose the biggest treatment-oriented liver cancer dataset for liver surgery and treatment planning. This dataset provides 216 cases (totally about 268K frames) scanned images in contrast-enhanced computed tomography (CT). We labeled all the CT images with the liver, liver vasculature and liver tumor segmentation ground truth for train and tune segmentation algorithms in advance. Based on that, we evaluate several recent and state-of-the-art segmentation algorithms, including 7 deep learning methods, on CT sequences. All results are compared to reference segmentations five error metrics that highlight different aspects of segmentation accuracy. In general, compared with previous datasets, our dataset is really a challenging dataset. To our knowledge, the proposed dataset and benchmark allow for the first time systematic exploration of such issues, and will be made available to allow for further research in this field.

A Novel Computer-Aided Diagnostic System for Early Assessment of Hepatocellular Carcinoma

Ahmed Alksas, Mohamed Shehata, Gehad Saleh, Ahmed Shaffie, Ahmed Soliman, Mohammed Ghazal, Hadil Abukhalifeh, Abdel Razek Ahmed, Ayman El-Baz
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 16:00 in session PS T5.8

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Auto-TLDR; Classification of Liver Tumor Lesions from CE-MRI Using Structured Structural Features and Functional Features

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Early assessment of liver cancer patients with hepatocellular carcinoma (HCC) is of immense importance to provide the proper treatment plan. In this paper, we have developed a two-stage classification computer-aided diagnostic (CAD) system that has the ability to detect and grade the liver observations from multiphase contrast enhanced magnetic resonance imaging (CE-MRI). The proposed approach consists of three main steps. First, a pre-processing is applied to the CE-MRI scans to delineate the tumor lesions that will be used as an ROI across the four different phases of the CE-MRI, (namely, the pre-contrast, late-arterial, portal-venous, and delayed-contrast). Second, a group of three features are modeled to provide a quantitative discrimination between the tumor lesions; namely: i) the tumor appearance that is modeled using a set of texture features, (namely; the first-order histogram, second-order gray-level co-occurrence matrix, and second-order gray-level run-length matrix), to capture any discrimination that may appear in the lesion texture, ii) the spherical harmonics (SH) based shape features that have the ability to describe the shape complexity of the liver tumors, and iii) the functional features that are based on the calculation of the wash-in/wash-out through that evaluate the intensity changes across the post-contrast phases. Finally, the aforementioned individual features were then integrated together to obtain the combined features to be fed to a machine learning classifier towards getting the final diagnostic decision. The proposed CAD system has been tested using hepatic observations that was obtained from 85 participating patients, 34 patients with benign tumors, 34 patients with intermediate tumors and 34 with malignant tumors. Using a random forests based classifier with a leave-one-subject-out (LOSO) cross-validation, the developed CAD system achieved an 87.1% accuracy in distinguishing the malignant, intermediate and benign tumors. The classification performance is then evaluated using k-fold (5/10-fold) cross-validation approach to examine the robustness of the system. The LR-1 lesions were classified from LR-2 benign lesions with 91.2% accuracy, while 85.3% accuracy was achieved differentiating between LR-4 and LR-5 malignant tumors. The obtained results hold a promise of the proposed framework to be reliably used as a noninvasive diagnostic tool for the early detection and grading of liver cancer tumors.

Automatic Semantic Segmentation of Structural Elements related to the Spinal Cord in the Lumbar Region by Using Convolutional Neural Networks

Jhon Jairo Sáenz Gamboa, Maria De La Iglesia-Vaya, Jon Ander Gómez
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 16:00 in session PS T5.6

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Auto-TLDR; Semantic Segmentation of Lumbar Spine Using Convolutional Neural Networks

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This work addresses the problem of automatically segmenting the MR images corresponding to the lumbar spine. The purpose is to detect and delimit the different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, etc. This task is known as semantic segmentation. The approach proposed in this work is based on convolutional neural networks whose output is a mask where each pixel from the input image is classified into one of the possible classes. Classes were defined by radiologists and correspond to structural elements and tissues. The proposed network architectures are variants of the U-Net. Several complementary blocks were used to define the variants: spatial attention models, deep supervision and multi-kernels at input, this last block type is based on the idea of inception. Those architectures which got the best results are described in this paper, and their results are discussed. Two of the proposed architectures outperform the standard U-Net used as baseline.

Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation

Martin Kolarik, Radim Burget, Carlos M. Travieso-Gonzalez, Jan Kocica
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 16:00 in session OS T1.6

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Auto-TLDR; Planar 3D Res-U-Net Network for Unbalanced 3D Image Segmentation using Fluid Attenuation Inversion Recover

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We present a novel approach of 2D to 3D transfer learning based on mapping pre-trained 2D convolutional neural network weights into planar 3D kernels. The method is validated by proposed planar 3D res-u-net network with encoder transferred from the 2D VGG-16 which is applied for a single-stage unbalanced 3D image data segmentation. In particular, we evaluate the method on the MICCAI 2016 MS lesion segmentation challenge dataset utilizing solely Fluid Attenuation Inversion Recover (FLAIR) sequence without brain extraction for training and inference to simulate real medical praxis. The planar 3D res-u-net network performed the best both in sensitivity and Dice score amongst end to end methods processing raw MRI scans and achieved comparable Dice score to a state-of-the-art unimodal not end to end approach. Complete source code was released under the open-source license and this paper is in compliance with the Machine learning Reproducibility Checklist. By implementing practical transfer learning for 3D data representation we were able to successfully segment heavily unbalanced data without selective sampling and achieved more reliable results using less training data in single modality. From medical perspective, the unimodal approach gives an advantage in real praxis as it does not require co-registration nor additional scanning time during examination. Although modern medical imaging methods capture high resolution 3D anatomy scans suitable for computer aided detection system processing, deployment of automatic systems for interpretation of radiology imaging is still rather theoretical in many medical areas. Our work aims to bridge the gap offering solution for partial research questions.

FOANet: A Focus of Attention Network with Application to Myocardium Segmentation

Zhou Zhao, Elodie Puybareau, Nicolas Boutry, Thierry Geraud
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 16:30 in session PS T5.4

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Auto-TLDR; FOANet: A Hybrid Loss Function for Myocardium Segmentation of Cardiac Magnetic Resonance Images

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In myocardium segmentation of cardiac magnetic resonance images, ambiguities often appear near the boundaries of the target domains due to tissue similarities. To address this issue, we propose a new architecture, called FOANet, which can be decomposed in three main steps: a localization step, a Gaussian-based contrast enhancement step, and a segmentation step. This architecture is supplied with a hybrid loss function that guides the FOANet to study the transformation relationship between the input image and the corresponding label in a threelevel hierarchy (pixel-, patch- and map-level), which is helpful to improve segmentation and recovery of the boundaries. We demonstrate the efficiency of our approach on two public datasets in terms of regional and boundary segmentations.

A Deep Learning Approach for the Segmentation of Myocardial Diseases

Khawala Brahim, Abdull Qayyum, Alain Lalande, Arnaud Boucher, Anis Sakly, Fabrice Meriaudeau
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 16:00 in session PS T5.6

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Auto-TLDR; Segmentation of Myocardium Infarction Using Late GADEMRI and SegU-Net

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Cardiac left ventricular (LV) segmentation is of paramount essential step for both diagnosis and treatment of cardiac pathologies such as ischemia, myocardial infarction, arrhythmia and myocarditis. However, this segmentation is challenging due to high variability across patients and the potential lack of contrast between structures. In this work, we propose and evaluate a (2.5D) SegU-Net model based on the fusion of two deep learning techniques (U-Net and Seg-Net) for automated LGEMRI (Late gadolinium enhanced magnetic resonance imaging) myocardial disease (infarct core and no reflow region) quantification in a new multifield expert annotated dataset. Given that the scar tissue represents a small part of the whole MRI slices, we focused on myocardium area. Segmentation results show that this preprocessing step facilitate the learning procedure. In order to solve the class imbalance problem, we propose to apply the Jaccard loss and the Focal Loss as optimization loss function and to integrate a class weights strategy into the objective function. Late combination has been used to merge the output of the best trained models on a different set of hyperparameters. The final network segmentation performances will be useful for future comparison of new method to the current related work for this task. A total number of 2237 of slices (320 cases) were used for training/validation and 210 slices (35 cases) were used for testing. Experiments over our proposed dataset, using several evaluation metrics such Jaccard distance (IOU), Accuracy and Dice similarity coefficient (DSC), demonstrate efficiency performance in quantifying different zones of myocardium infarction across various patients. As compared to the second intra-observer study, our testing results showed that the SegUNet prediction model leads to these average dice coefficients over all segmented tissue classes, respectively : 'Background': 0.99999, 'Myocardium': 0.99434, 'Infarctus': 0.95587, 'Noreflow': 0.78187.

One-Stage Multi-Task Detector for 3D Cardiac MR Imaging

Weizeng Lu, Xi Jia, Wei Chen, Nicolò Savioli, Antonio De Marvao, Linlin Shen, Declan O'Regan, Jinming Duan
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 12:00 in session PS T5.5

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Auto-TLDR; Multi-task Learning for Real-Time, simultaneous landmark location and bounding box detection in 3D space

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Fast and accurate landmark location and bounding box detection are important steps in 3D medical imaging. In this paper, we propose a novel multi-task learning framework, for real-time, simultaneous landmark location and bounding box detection in 3D space. Our method extends the famous single-shot multibox detector (SSD) from single-task learning to multi-task learning and from 2D to 3D. Furthermore, we propose a post-processing approach to refine the network landmark output, by averaging the candidate landmarks. Owing to these settings, the proposed framework is fast and accurate. For 3D cardiac magnetic resonance (MR) images with size 224 × 224 × 64, our framework runs about 128 volumes per second (VPS) on GPU and achieves 6.75mm average point-to-point distance error for landmark location, which outperforms both state-of-the-art and baseline methods. We also show that segmenting the 3D image cropped with the bounding box results in both improved performance and efficiency.

Segmentation of Intracranial Aneurysm Remnant in MRA Using Dual-Attention Atrous Net

Subhashis Banerjee, Ashis Kumar Dhara, Johan Wikström, Robin Strand
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 16:30 in session PS T5.4

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Auto-TLDR; Dual-Attention Atrous Net for Segmentation of Intracranial Aneurysm Remnant from MRA Images

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Due to the advancement of non-invasive medical imaging modalities like Magnetic Resonance Angiography (MRA), an increasing number of Intracranial Aneurysm (IA) cases are being reported in recent years. The IAs are typically treated by so-called endovascular coiling, where blood flow in the IA is prevented by embolization with a platinum coil. Accurate quantification of the IA Remnant (IAR), i.e. the volume with blood flow present post treatment is the utmost important factor in choosing the right treatment planning. This is typically done by manually segmenting the aneurysm remnant from the MRA volume. Since manual segmentation of volumetric images is a labour-intensive and error-prone process, development of an automatic volumetric segmentation method is required. Segmentation of small structures such as IA, that may largely vary in size, shape, and location is considered extremely difficult. Similar intensity distribution of IAs and surrounding blood vessels makes it more challenging and susceptible to false positive. In this paper we propose a novel 3D CNN architecture called Dual-Attention Atrous Net (DAtt-ANet), which can efficiently segment IAR volumes from MRA images by reconciling features at different scales using the proposed Parallel Atrous Unit (PAU) along with the use of self-attention mechanism for extracting fine-grained features and intra-class correlation. The proposed DAtt-ANet model is trained and evaluated on a clinical MRA image dataset (prospective research project, approved by the local ethical committee) of IAR consisting of 46 subjects, annotated by an expert radiologist from our group. We compared the proposed DAtt-ANet with five state-of-the-art CNN models based on their segmentation performance. The proposed DAtt-ANet outperformed all other methods and was able to achieve a five-fold cross-validation DICE score of $0.73\pm0.06$.

Segmentation of Axillary and Supraclavicular Tumoral Lymph Nodes in PET/CT: A Hybrid CNN/Component-Tree Approach

Diana Lucia Farfan Cabrera, Nicolas Gogin, David Morland, Benoît Naegel, Dimitri Papathanassiou, Nicolas Passat
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 13:00 in session OS T5.5

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Auto-TLDR; Coupling Convolutional Neural Networks and Component-Trees for Lymph node Segmentation from PET/CT Images

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The analysis of axillary and supraclavicular lymph nodes is a primary prognostic factor for the staging of breast cancer. However, due to the size of lymph nodes and the low resolution of PET data, their segmentation is challenging. We investigate the relevance of considering axillary and supraclavicular lymph node segmentation from PET/CT images by coupling Convolutional Neural Networks (CNNs) and Component-Trees (C-Trees). Building upon the U-Net architecture, we propose a framework that couples a multi-modal U-Net fed with PET and CT, coupled with a hierarchical model obtained from the PET that provides additional high-level region-based features as input channels. Our working hypotheses are twofold. First, we take advantage of both anatomical information from CT for detecting the nodes, and from functional information from PET for detecting the pathological ones. Second, we consider region-based attributes extracted from C-Tree analysis of 3D PET/CT images to improve the CNN segmentation. We carried out experiments on a dataset of 240 pathological lymph nodes from 52 patients scans, and compared our outputs with human expert-defined ground-truth, leading to promising results.

DARN: Deep Attentive Refinement Network for Liver Tumor Segmentation from 3D CT Volume

Yao Zhang, Jiang Tian, Cheng Zhong, Yang Zhang, Zhongchao Shi, Zhiqiang He
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.2

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Auto-TLDR; Deep Attentive Refinement Network for Liver Tumor Segmentation from 3D Computed Tomography Using Multi-Level Features

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Automatic liver tumor segmentation from 3D Computed Tomography (CT) is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. However, accurate liver tumor segmentation remains challenging due to the large variability of tumor sizes and inhomogeneous texture. Recent advances based on Fully Convolutional Network (FCN) in liver tumor segmentation draw on success of learning discriminative multi-level features. In this paper, we propose a Deep Attentive Refinement Network (DARN) for improved liver tumor segmentation from CT volumes by fully exploiting both low and high level features embedded in different layers of FCN. Different from existing works, we exploit attention mechanism to leverage the relation of different levels of features encoded in different layers of FCN. Specifically, we introduce a Semantic Attention Refinement (SemRef) module to selectively emphasize global semantic information in low level features with the guidance of high level ones, and a Spatial Attention Refinement (SpaRef) module to adaptively enhance spatial details in high level features with the guidance of low level ones. We evaluate our network on the public MICCAI 2017 Liver Tumor Segmentation Challenge dataset (LiTS dataset) and it achieves state-of-the-art performance. The proposed refinement modules are an effective strategy to exploit multi-level features and has great potential to generalize to other medical image segmentation tasks.

BCAU-Net: A Novel Architecture with Binary Channel Attention Module for MRI Brain Segmentation

Yongpei Zhu, Zicong Zhou, Guojun Liao, Kehong Yuan
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 12:00 in session PS T5.5

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Auto-TLDR; BCAU-Net: Binary Channel Attention U-Net for MRI brain segmentation

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Recently deep learning-based networks have achieved advanced performance in medical image segmentation. However, the development of deep learning is slow in magnetic resonance image (MRI) segmentation of normal brain tissues. In this paper, inspired by channel attention module, we propose a new architecture, Binary Channel Attention U-Net (BCAU-Net), by introducing a novel Binary Channel Attention Module (BCAM) into skip connection of U-Net, which can take full advantages of the channel information extracted from the encoding path and corresponding decoding path. To better aggregate multi-scale spatial information of the feature map, spatial pyramid pooling (SPP) modules with different pooling operations are used in BCAM instead of original average-pooling and max-pooling operations. We verify this model on two datasets including IBSR and MRBrainS18, and obtain better performance on MRI brain segmentation compared with other methods. We believe the proposed method can advance the performance in brain segmentation and clinical diagnosis.

Offset Curves Loss for Imbalanced Problem in Medical Segmentation

Ngan Le, Duc Toan Bui, Khoa Luu, Marios Savvides
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.1

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Auto-TLDR; Offset Curves Loss for Medical Image Segmentation

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Medical image segmentation has played an important role in medical analysis and widely developed for many clinical applications. Deep learning-based approaches have achieved high performance in semantic segmentation but they are limited to pixel-wise setting and imbalanced classes data problem. In this paper, we tackle those limitations by developing a new deep learning-based model which takes into account both higher feature level i.e. region inside contour, intermediate feature level i.e. offset curves around the contour and lower feature level i.e. contour. Our proposed Offset Curves (OsC) loss consists of three main fitting terms. The first fitting term focuses on pixel-wise level segmentation whereas the second fitting term acts as attention model which pays attention to the area around the boundaries (offset curves). The third terms plays a role as regularization term which takes the length of boundaries into account. We evaluate our proposed OsC loss on both 2D network and 3D network. Two common medical datasets, i.e. retina DRIVE and brain tumor BRATS 2018 datasets are used to benchmark our proposed loss performance. The experiments have showed that our proposed OsC loss function outperforms other mainstream loss functions such as Cross-Entropy, Dice, Focal on the most common segmentation networks Unet, FCN.

A Deep Learning-Based Method for Predicting Volumes of Nasopharyngeal Carcinoma for Adaptive Radiation Therapy Treatment

Bilel Daoud, Ken'Ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat, Jamel Daoud
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Fri 15 Jan 2021 at 15:00 in session PS T1.14

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Auto-TLDR; TEP-Net: Tumor Evolution Prediction of Nasopharyngeal Carcinoma and Organ-at-risks Using CT Images

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This paper presents a new system for predicting the spatial change of Nasopharyngeal carcinoma(NPC) and organ-at-risks (OARs) volumes over the course of the radiation therapy (RT) treatment for facilitating the workflow of adaptive radiotherapy. The proposed system, called " Tumor Evolution Prediction (TEP-Net)", predicts the spatial distributions of NPC and 5 OARs, separately, in response to RT in the coming week, week n. Here, TEP-Net has (n-1)-inputs that are week 1 to week n-1 of CT axial, coronal or sagittal images acquired once the patient complete the planned RT treatment of the corresponding week. As a result, three predicted results of each target region are obtained from the three-view CT images. To determine the final prediction of NPC and 5 OARs, two integration methods, weighted fully connected layers and weighted voting methods, are introduced. From the experiments using weekly CT images of 140 NPC patients, our proposed system achieves the best performance for predicting NPC and OARs compared with conventional methods.

End-To-End Multi-Task Learning for Lung Nodule Segmentation and Diagnosis

Wei Chen, Qiuli Wang, Dan Yang, Xiaohong Zhang, Chen Liu, Yucong Li
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 13:00 in session OS T5.5

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Auto-TLDR; A novel multi-task framework for lung nodule diagnosis based on deep learning and medical features

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Computer-Aided Diagnosis (CAD) systems for lung nodule diagnosis based on deep learning have attracted much attention in recent years. However, most existing methods ignore the relationships between the segmentation and classification tasks, which leads to unstable performances. To address this problem, we propose a novel multi-task framework, which can provide lung nodule segmentation mask, malignancy prediction, and medical features for interpretable diagnosis at the same time. Our framework mainly contains two sub-network: (1) Multi-Channel Segmentation Sub-network (MSN) for lung nodule segmentation, and (2) Joint Classification Sub-network (JCN) for interpretable lung nodule diagnosis. In the proposed framework, we use U-Net down-sampling processes for extracting low-level deep learning features, which are shared by two sub-networks. The JCN forces the down-sampling processes to learn better lowlevel deep features, which lead to a better construct of segmentation masks. Meanwhile, two additional channels constructed by OTSU and super-pixel (SLIC) methods, are utilized as the guideline of the feature extraction. The proposed framework takes advantages of deep learning methods and classical methods, which can significantly improve the performances of all tasks. We evaluate the proposed framework on public dataset LIDCIDRI. Our framework achieves a promising Dice score of 86.43% in segmentation, 87.07% in malignancy level prediction, and convincing results in interpretable medical feature predictions.

Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval

Stefano Allegretti, Federico Bolelli, Federico Pollastri, Sabrina Longhitano, Giovanni Pellacani, Costantino Grana
Track 3: Computer Vision Robotics and Intelligent Systems
Wed 13 Jan 2021 at 16:30 in session PS T3.5

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Auto-TLDR; Skin Images Retrieval Using Convolutional Neural Networks for Skin Lesion Classification and Segmentation

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Given the relevance of skin cancer, many attempts have been dedicated to the creation of automated devices that could assist both expert and beginner dermatologists towards fast and early diagnosis of skin lesions. In recent years, tasks such as skin lesion classification and segmentation have been extensively addressed with deep learning algorithms, which in some cases reach a diagnostic accuracy comparable to that of expert physicians. However, the general lack of interpretability and reliability severely hinders the ability of those approaches to actually support dermatologists in the diagnosis process. In this paper a novel skin images retrieval system is presented, which exploits features extracted by Convolutional Neural Networks to gather similar images from a publicly available dataset, in order to assist the diagnosis process of both expert and novice practitioners. In the proposed framework, Resnet-50 is initially trained for the classification of dermoscopic images; then, the feature extraction part is isolated, and an embedding network is build on top of it. The embedding learns an alternative representation, which allows to check image similarity by means of a distance measure. Experimental results reveal that the proposed method is able to select meaningful images, which can effectively boost the classification accuracy of human dermatologists.

Classify Breast Histopathology Images with Ductal Instance-Oriented Pipeline

Beibin Li, Ezgi Mercan, Sachin Mehta, Stevan Knezevich, Corey Arnold, Donald Weaver, Joann Elmore, Linda Shapiro
Track 3: Computer Vision Robotics and Intelligent Systems
Wed 13 Jan 2021 at 16:30 in session PS T3.6

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Auto-TLDR; DIOP: Ductal Instance-Oriented Pipeline for Diagnostic Classification

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In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification. Based on recent advancements in instance segmentation and the Mask R-CNN model, our duct-level segmenter tries to identify each ductal individual inside a microscopic image; then, it extracts tissue-level information from the identified ductal instances. Leveraging three levels of information obtained from these ductal instances and also the histopathology image, the proposed DIOP outperforms previous approaches (both feature-based and CNN-based) in all diagnostic tasks; for the four-way classification task, the DIOP achieves comparable performance to general pathologists in this unique dataset. The proposed DIOP only takes a few seconds to run in the inference time, which could be used interactively on most modern computers. More clinical explorations are needed to study the robustness and generalizability of this system in the future.

Confidence Calibration for Deep Renal Biopsy Immunofluorescence Image Classification

Federico Pollastri, Juan Maroñas, Federico Bolelli, Giulia Ligabue, Roberto Paredes, Riccardo Magistroni, Costantino Grana
Track 3: Computer Vision Robotics and Intelligent Systems
Wed 13 Jan 2021 at 16:30 in session PS T3.5

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Auto-TLDR; A Probabilistic Convolutional Neural Network for Immunofluorescence Classification in Renal Biopsy

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With this work we tackle immunofluorescence classification in renal biopsy, employing state-of-the-art Convolutional Neural Networks. In this setting, the aim of the probabilistic model is to assist an expert practitioner towards identifying the location pattern of antibody deposits within a glomerulus. Since modern neural networks often provide overconfident outputs, we stress the importance of having a reliable prediction, demonstrating that Temperature Scaling, a recently introduced re-calibration technique, can be successfully applied to immunofluorescence classification in renal biopsy. Experimental results demonstrate that the designed model yields good accuracy on the specific task, and that Temperature Scaling is able to provide reliable probabilities, which are highly valuable for such a task given the low inter-rater agreement.

Bridging the Gap between Natural and Medical Images through Deep Colorization

Lia Morra, Luca Piano, Fabrizio Lamberti, Tatiana Tommasi
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 16:30 in session PS T5.4

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Auto-TLDR; Transfer Learning for Diagnosis on X-ray Images Using Color Adaptation

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Deep learning has thrived by training on large-scale datasets. However, in many applications, as for medical image diagnosis, getting massive amount of data is still prohibitive due to privacy, lack of acquisition homogeneity and annotation cost. In this scenario transfer learning from natural image collections is a standard practice that attempts to tackle shape, texture and color discrepancy all at once through pretrained model fine-tuning. In this work we propose to disentangle those challenges and design a dedicated network module that focuses on color adaptation. We combine learning from scratch of the color module with transfer learning of different classification backbones obtaining an end-to-end, easy-to-train architecture for diagnostic image recognition on X-ray images. Extensive experiments show how our approach is particularly efficient in case of data scarcity and provides a new path for further transferring the learned color information across multiple medical datasets.

Deep Recurrent-Convolutional Model for AutomatedSegmentation of Craniomaxillofacial CT Scans

Francesca Murabito, Simone Palazzo, Federica Salanitri Proietto, Francesco Rundo, Ulas Bagci, Daniela Giordano, Rosalia Leonardi, Concetto Spampinato
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 16:30 in session PS T5.4

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Auto-TLDR; Automated Segmentation of Anatomical Structures in Craniomaxillofacial CT Scans using Fully Convolutional Deep Networks

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In this paper we define a deep learning architecture for automated segmentation of anatomical structures in Craniomaxillofacial (CMF) CT scans that leverages the recent success of encoder-decoder models for semantic segmentation of natural images. In particular, we propose a fully convolutional deep network that combines the advantages of recent fully convolutional models, such as Tiramisu, with squeeze-and-excitation blocks for feature recalibration, integrated with convolutional LSTMs to model spatio-temporal correlations between consecutive slices. The proposed segmentation network shows superior performance and generalization capabilities (to different structures and imaging modalities) than state of the art methods on automated segmentation of CMF structures (e.g., mandibles and airways) in several standard benchmarks (e.g., MICCAI datasets) and on new datasets proposed herein, effectively facing shape variability.

MTGAN: Mask and Texture-Driven Generative Adversarial Network for Lung Nodule Segmentation

Wei Chen, Qiuli Wang, Kun Wang, Dan Yang, Xiaohong Zhang, Chen Liu, Yucong Li
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 12:00 in session PS T5.3

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Auto-TLDR; Mask and Texture-driven Generative Adversarial Network for Lung Nodule Segmentation

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Accurate segmentation for lung nodules in lung computed tomography (CT) scans plays a key role in the early diagnosis of lung cancer. Many existing methods, especially UNet, have made significant progress in lung nodule segmentation. However, due to the complex shapes of lung nodules and the similarity of visual characteristics between nodules and lung tissues, an accurate segmentation with few false positives of lung nodules is still a challenging problem. Considering the fact that both boundary and texture information of lung nodules are important for obtaining an accurate segmentation result, we propose a novel Mask and Texture-driven Generative Adversarial Network (MTGAN) with a joint multi-scale L1 loss for lung nodule segmentation, which takes full advantages of U-Net and adversarial training. The proposed MTGAN leverages adversarial learning strategy guided by the boundary and texture information of lung nodules to generate more accurate segmentation results with lesser false positives. We validate our model with the LIDC–IDRI dataset, and experimental results show that our method achieves excellent segmentation results for a variety of lung nodules, especially for juxtapleural nodules and low-dense nodules. Without any bells and whistles, the proposed MTGAN achieves significant segmentation performance with the Dice similarity coefficient (DSC) of 85.24% on the LIDC–IDRI dataset.

Do Not Treat Boundaries and Regions Differently: An Example on Heart Left Atrial Segmentation

Zhou Zhao, Elodie Puybareau, Nicolas Boutry, Thierry Geraud
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 13:00 in session OS T5.5

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Auto-TLDR; Attention Full Convolutional Network for Atrial Segmentation using ResNet-101 Architecture

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Atrial fibrillation is the most common heart rhythm disease. Due to a lack of understanding in matter of underlying atrial structures, current treatments are still not satisfying. Recently, with the popularity of deep learning, many segmentation methods based on fully convolutional networks have been proposed to analyze atrial structures, especially from late gadolinium-enhanced magnetic resonance imaging. However, two problems still occur: 1) segmentation results include the atrial-like background; 2) boundaries are very hard to segment. Most segmentation approaches design a specific network that mainly focuses on the regions, to the detriment of the boundaries. Therefore, this paper proposes an attention full convolutional network framework based on the ResNet-101 architecture, which focuses on boundaries as much as on regions. The additional attention module is added to have the network pay more attention on regions and then to reduce the impact of the misleading similarity of neighboring tissues. We also use a hybrid loss composed of a region loss and a boundary loss to treat boundaries and regions at the same time. We demonstrate the efficiency of the proposed approach on the MICCAI 2018 Atrial Segmentation Challenge public dataset.

Segmenting Kidney on Multiple Phase CT Images Using ULBNet

Yanling Chi, Yuyu Xu, Gang Feng, Jiawei Mao, Sihua Wu, Guibin Xu, Weimin Huang
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 12:00 in session PS T1.4

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Auto-TLDR; A ULBNet network for kidney segmentation on multiple phase CT images

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Abstract—Segmentation of kidney on CT images is critical to computer-assisted surgical planning for kidney interventional therapy. Segmenting kidney manually is impractical in clinical, automatic segmentation is desirable. U-Net has been successful in medical image segmentation and is a promising candidate for the task. However, semantic gap still exists, especially when multiple phase images or multiple center images are involved. In this paper, we proposed an ULBNet to reduce the semantic gap and to improve segmentation performance. The proposed architecture includes new skip connections of local binary convolution (LBC). We also proposed a novel strategy of fast retraining a model for a new task without manually labelling required. We evaluated the network for kidney segmentation on multiple phase CT images. ULBNet resulted in an overall accuracy of 98.0% with comparison to Resunet 97.5%. Specifically, on the plain phase CT images, 98.1% resulted from ULBNet and 97.6% from Resunet; on the corticomedullay phase images, 97.8% from ULBNet and 97.2% from Resunet; on the nephrographic phase images, 97.6% from ULBNet and 97.4% from Resunet; on the excretory phase images, 98.1% from ULBNet and 97.4% from Resunet. The proposed network architecture performs better than Resunet on generalizing to multiple phase images.

A Transformer-Based Network for Anisotropic 3D Medical Image Segmentation

Guo Danfeng, Demetri Terzopoulos
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.1

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Auto-TLDR; A transformer-based model to tackle the anisotropy problem in 3D medical image analysis

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A critical challenge of applying neural networks to 3D medical image analysis is to deal with the anisotropy problem. The inter-slice contextual information contained in medical images is important, especially when the structural information of lesions is needed. However, such information often varies with cases because of variable slice spacing. Image anisotropy downgrades model performance especially when slice spacing varies significantly among training and testing datasets. ExsiWe proposed a transformer-based model to tackle the anisotropy problem. It is adaptable to different levels of anisotropy and is computationally efficient. Experiments are conducted on 3D lung cancer segmentation task. Our model achieves an average Dice score of approximately 0.87, which generally outperforms baseline models.

A Lumen Segmentation Method in Ureteroscopy Images Based on a Deep Residual U-Net Architecture

Jorge Lazo, Marzullo Aldo, Sara Moccia, Michele Catellani, Benoit Rosa, Elena De Momi, Michel De Mathelin, Francesco Calimeri
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 16:30 in session PS T1.8

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Auto-TLDR; A Deep Neural Network for Ureteroscopy with Residual Units

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Ureteroscopy is becoming the first surgical treatment option for the majority of urinary affections. This procedure is carried out using an endoscope which provides the surgeon with the visual and spatial information necessary to navigate inside the urinary tract. Having in mind the development of surgical assistance systems, that could enhance the performance of surgeon, the task of lumen segmentation is a fundamental part since this is the visual reference which marks the path that the endoscope should follow. This is something that has not been analyzed in ureteroscopy data before. However, this task presents several challenges given the image quality and the conditions itself of ureteroscopy procedures. In this paper, we study the implementation of a Deep Neural Network which exploits the advantage of residual units in an architecture based on U-Net. For the training of these networks, we analyze the use of two different color spaces: gray-scale and RGB data images. We found that training on gray-scale images gives the best results obtaining mean values of Dice Score, Precision, and Recall of 0.73, 0.58, and 0.92 respectively. The results obtained show that the use of residual U-Net could be a suitable model for further development for a computer-aided system for navigation and guidance through the urinary system.

Adversarial Encoder-Multi-Task-Decoder for Multi-Stage Processes

Andre Mendes, Julian Togelius, Leandro Dos Santos Coelho
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Fri 15 Jan 2021 at 15:00 in session PS T1.13

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Auto-TLDR; Multi-Task Learning and Semi-Supervised Learning for Multi-Stage Processes

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In multi-stage processes, decisions occur in an ordered sequence of stages. Early stages usually have more observations with general information (easier/cheaper to collect), while later stages have fewer observations but more specific data. This situation can be represented by a dual funnel structure, in which the sample size decreases from one stage to the other while the information increases. Training classifiers in this scenario is challenging since information in the early stages may not contain distinct patterns to learn (underfitting). In contrast, the small sample size in later stages can cause overfitting. We address both cases by introducing a framework that combines adversarial autoencoders (AAE), multi-task learning (MTL), and multi-label semi-supervised learning (MLSSL). We improve the decoder of the AAE with an MTL component so it can jointly reconstruct the original input and use feature nets to predict the features for the next stages. We also introduce a sequence constraint in the output of an MLSSL classifier to guarantee the sequential pattern in the predictions. Using real-world data from different domains (selection process, medical diagnosis), we show that our approach outperforms other state-of-the-art methods.

Unsupervised Detection of Pulmonary Opacities for Computer-Aided Diagnosis of COVID-19 on CT Images

Rui Xu, Xiao Cao, Yufeng Wang, Yen-Wei Chen, Xinchen Ye, Lin Lin, Wenchao Zhu, Chao Chen, Fangyi Xu, Yong Zhou, Hongjie Hu, Shoji Kido, Noriyuki Tomiyama
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 12:00 in session PS T5.3

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Auto-TLDR; A computer-aided diagnosis of COVID-19 from CT images using unsupervised pulmonary opacity detection

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COVID-19 emerged towards the end of 2019 which was identified as a global pandemic by the world heath organization (WHO). With the rapid spread of COVID-19, the number of infected and suspected patients has increased dramatically. Chest computed tomography (CT) has been recognized as an efficient tool for the diagnosis of COVID-19. However, the huge CT data make it difficult for radiologist to fully exploit them on the diagnosis. In this paper, we propose a computer-aided diagnosis system that can automatically analyze CT images to distinguish the COVID-19 against to community-acquired pneumonia (CAP). The proposed system is based on an unsupervised pulmonary opacity detection method that locates opacity regions by a detector unsupervisedly trained from CT images with normal lung tissues. Radiomics based features are extracted insides the opacity regions, and fed into classifiers for classification. We evaluate the proposed CAD system by using 200 CT images collected from different patients in several hospitals. The accuracy, precision, recall, f1-score and AUC achieved are 95.5%, 100%, 91%, 95.1% and 95.9% respectively, exhibiting the promising capacity on the differential diagnosis of COVID-19 from CT images.

A Systematic Investigation on Deep Architectures for Automatic Skin Lesions Classification

Pierluigi Carcagni, Marco Leo, Andrea Cuna, Giuseppe Celeste, Cosimo Distante
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 16:30 in session PS T5.4

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Auto-TLDR; RegNet: Deep Investigation of Convolutional Neural Networks for Automatic Classification of Skin Lesions

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Computer vision-based techniques are more and more employed in healthcare and medical fields nowadays in order, principally, to be as a support to the experienced medical staff to help them to make a quick and correct diagnosis. One of the hot topics in this arena concerns the automatic classification of skin lesions. Several promising works exist about it, mainly leveraging Convolutional Neural Networks (CNN), but proposed pipeline mainly rely on complex data preprocessing and there is no systematic investigation about how available deep models can actually reach the accuracy needed for real applications. In order to overcome these drawbacks, in this work, an end-to-end pipeline is introduced and some of the most recent Convolutional Neural Networks (CNNs) architectures are included in it and compared on the largest common benchmark dataset recently introduced. To this aim, for the first time in this application context, a new network design paradigm, namely RegNet, has been exploited to get the best models among a population of configurations. The paper introduces a threefold level of contribution and novelty with respect the previous literature: the deep investigation of several CNN architectures driving to a consistent improvement of the lesions recognition accuracy, the exploitation of a new network design paradigm able to study the behavior of populations of models and a deep discussion about pro and cons of each analyzed method paving the path towards new research lines.

Breast Anatomy Enriched Tumor Saliency Estimation

Fei Xu, Yingtao Zhang, Heng-Da Cheng, Jianrui Ding, Boyu Zhang, Chunping Ning, Ying Wang
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.1

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Auto-TLDR; Tumor Saliency Estimation for Breast Ultrasound using enriched breast anatomy knowledge

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Breast cancer investigation is of great significance and developing tumor detection methodologies is a critical need. However, it is a challenging task for breast cancer detection using breast ultrasound (BUS) images due to the complicated breast structure and poor quality of the images. In this paper, we propose a novel tumor saliency estimation (TSE) model guided by enriched breast anatomy knowledge to localize the tumor. First, the breast anatomy layers are generated by a deep neural network. Then we refine the layers by integrating a non-semantic breast anatomy model to solve the problems of incomplete mammary layers. Meanwhile, a new background map generation method weighted by the semantic probability and spatial distance is proposed to improve the performance. The experiment demonstrates that the proposed method with the new background map outperforms four state-of-the-art TSE models with increasing 10% of F_meansure on the public BUS dataset.

Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks

Michele Alberti, Angela Botros, Schuetz Narayan, Rolf Ingold, Marcus Liwicki, Mathias Seuret
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Tue 12 Jan 2021 at 15:00 in session PS T1.2

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Auto-TLDR; Trainable and Spectrally Initializable Matrix Transformations for Neural Networks

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In this work, we introduce a new architectural component to Neural Networks (NN), i.e., trainable and spectrally initializable matrix transformations on feature maps. While previous literature has already demonstrated the possibility of adding static spectral transformations as feature processors, our focus is on more general trainable transforms. We study the transforms in various architectural configurations on four datasets of different nature: from medical (ColorectalHist, HAM10000) and natural (Flowers) images to historical documents (CB55). With rigorous experiments that control for the number of parameters and randomness, we show that networks utilizing the introduced matrix transformations outperform vanilla neural networks. The observed accuracy increases appreciably across all datasets. In addition, we show that the benefit of spectral initialization leads to significantly faster convergence, as opposed to randomly initialized matrix transformations. The transformations are implemented as auto-differentiable PyTorch modules that can be incorporated into any neural network architecture. The entire code base is open-source.

Dual Stream Network with Selective Optimization for Skin Disease Recognition in Consumer Grade Images

Krishnam Gupta, Jaiprasad Rampure, Monu Krishnan, Ajit Narayanan, Nikhil Narayan
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 16:00 in session PS T5.6

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Auto-TLDR; A Deep Network Architecture for Skin Disease Localisation and Classification on Consumer Grade Images

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Skin disease localisation and classification on consumer-grade images is more challenging compared to that on dermoscopic imaging. Consumer grade images refer to the images taken using commonly available imaging devices such as a mobile camera or a hand held digital camera. Such images, in addition to having the skin condition of interest in a very small area of the image, has other noisy non-clinical details introduced due to the lighting conditions and the distance of the hand held device from the anatomy at the time of acquisition. We propose a novel deep network architecture \& a new optimization strategy for classification with implicit localisation of skin diseases from clinical/consumer grade images. A weakly supervised segmentation algorithm is first employed to extract Region of Interests (RoI) from the image, the RoI and the original image form the two input streams of the proposed architecture. Each stream of the architecture learns high level and low level features from the original image and the RoI, respectively. The two streams are independently optimised until the loss stops decreasing after which both the streams are optimised collectively with the help of a third combiner sub-network. Such a strategy resulted in a 5% increase of accuracy over the current state-of-the-art methods on SD-198 dataset, which is publicly available. The proposed algorithm is also validated on a new dataset containing over 12,000 images across 75 different skin conditions. We intend to release this dataset as SD-75 to aid in the advancement of research on skin condition classification on consumer grade images.

Learning to Segment Clustered Amoeboid Cells from Brightfield Microscopy Via Multi-Task Learning with Adaptive Weight Selection

Rituparna Sarkar, Suvadip Mukherjee, Elisabeth Labruyere, Jean-Christophe Olivo-Marin
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 16:00 in session PS T5.8

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Auto-TLDR; Supervised Cell Segmentation from Microscopy Images using Multi-task Learning in a Multi-Task Learning Paradigm

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Detecting and segmenting individual cells from microscopy images is critical to various life science applications. Traditional cell segmentation tools are often ill-suited for applications in brightfield microscopy due to poor contrast and intensity heterogeneity, and only a small subset are applicable to segment cells in a cluster. In this regard, we introduce a novel supervised technique for cell segmentation in a multi-task learning paradigm. A combination of a multi-task loss, based on the region and cell boundary detection, is employed for an improved prediction efficiency of the network. The learning problem is posed in a novel min-max framework which enables adaptive estimation of the hyper-parameters in an automatic fashion. The region and cell boundary predictions are combined via morphological operations and active contour model to segment individual cells. The proposed methodology is particularly suited to segment touching cells from brightfield microscopy images without manual interventions. Quantitatively, we observe an overall Dice score of 0.93 on the validation set, which is an improvement of over 15.9% on a recent unsupervised method, and outperforms the popular supervised U-net algorithm by at least 5.8% on average.

A Comparison of Neural Network Approaches for Melanoma Classification

Maria Frasca, Michele Nappi, Michele Risi, Genoveffa Tortora, Alessia Auriemma Citarella
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.2

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Auto-TLDR; Classification of Melanoma Using Deep Neural Network Methodologies

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Melanoma is the deadliest form of skin cancer and it is diagnosed mainly visually, starting from initial clinical screening and followed by dermoscopic analysis, biopsy and histopathological examination. A dermatologist’s recognition of melanoma may be subject to errors and may take some time to diagnose it. In this regard, deep learning can be useful in the study and classification of skin cancer. In particular, by classifying images with Deep Neural Network methodologies, it is possible to obtain comparable or even superior results compared to those of dermatologists. In this paper, we propose a methodology for the classification of melanoma by adopting different deep learning techniques applied to a common dataset, composed of images from the ISIC dataset and consisting of different types of skin diseases, including melanoma on which we applied a specific pre-processing phase. In particular, a comparison of the results is performed in order to select the best effective neural network to be applied to the problem of recognition and classification of melanoma. Moreover, we also evaluate the impact of the pre- processing phase on the final classification. Different metrics such as accuracy, sensitivity, and specificity have been selected to assess the goodness of the adopted neural networks and compare them also with the manual classification of dermatologists.

Learn to Segment Retinal Lesions and Beyond

Qijie Wei, Xirong Li, Weihong Yu, Xiao Zhang, Yongpeng Zhang, Bojie Hu, Bin Mo, Di Gong, Ning Chen, Dayong Ding, Youxin Chen
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.2

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Auto-TLDR; Multi-task Lesion Segmentation and Disease Classification for Diabetic Retinopathy Grading

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Towards automated retinal screening, this paper makes an endeavor to simultaneously achieve pixel-level retinal lesion segmentation and image-level disease classification. Such a multi-task approach is crucial for accurate and clinically interpretable disease diagnosis. Prior art is insufficient due to three challenges, i.e., lesions lacking objective boundaries, clinical importance of lesions irrelevant to their size, and the lack of one-to-one correspondence between lesion and disease classes. This paper attacks the three challenges in the context of diabetic retinopathy (DR) grading. We propose Lesion-Net, a new variant of fully convolutional networks, with its expansive path re- designed to tackle the first challenge. A dual Dice loss that leverages both semantic segmentation and image classification losses is introduced to resolve the second challenge. Lastly, we build a multi-task network that employs Lesion-Net as a side- attention branch for both DR grading and result interpretation. A set of 12K fundus images is manually segmented by 45 ophthalmologists for 8 DR-related lesions, resulting in 290K manual segments in total. Extensive experiments on this large- scale dataset show that our proposed approach surpasses the prior art for multiple tasks including lesion segmentation, lesion classification and DR grading.

Predicting Chemical Properties Using Self-Attention Multi-Task Learning Based on SMILES Representation

Sangrak Lim, Yong Oh Lee
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Fri 15 Jan 2021 at 15:00 in session PS T1.14

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Auto-TLDR; Self-attention based Transformer-Variant Model for Chemical Compound Properties Prediction

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In the computational prediction of chemical compound properties, molecular descriptors and fingerprints encoded to low dimensional vectors are used. The selection of proper molecular descriptors and fingerprints is both important and challenging as the performance of such models is highly dependent on descriptors. To overcome this challenge, natural language processing models that utilize simplified molecular input line entry system as input were studied, and several transformer variant models achieved superior results when compared with conventional methods. In this study, we explored the structural differences of the transformer-variant model and proposed a new self-attention based model. The representation learning performance of the self-attention module was evaluated in a multi-task learning environment using imbalanced chemical datasets. The experiment results showed that our model achieved competitive outcomes on several benchmark datasets. The source code of our experiment is available at https://github.com/arwhirang/sa-mtl and the dataset is available from the same URL.

Semantic Segmentation of Breast Ultrasound Image with Pyramid Fuzzy Uncertainty Reduction and Direction Connectedness Feature

Kuan Huang, Yingtao Zhang, Heng-Da Cheng, Ping Xing, Boyu Zhang
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.1

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Auto-TLDR; Uncertainty-Based Deep Learning for Breast Ultrasound Image Segmentation

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Deep learning approaches have achieved impressive results in breast ultrasound (BUS) image segmentation. However, these methods did not solve uncertainty and noise in BUS images well. To address this issue, we present a novel deep learning structure for BUS image semantic segmentation by analyzing the uncertainty using a pyramid fuzzy block and generating a novel feature based on connectedness. Firstly, feature maps in the proposed network are down-sampled to different resolutions. Fuzzy transformation and uncertainty representation are applied to each resolution to obtain the uncertainty degree on different scales. Meanwhile, the BUS images contain layer structures. From top to bottom, there are skin layer, fat layer, mammary layer, muscle layer, and background area. A spatial recurrent neural network (RNN) is utilized to calculate the connectedness between each pixel and the pixels on the four boundaries in horizontal and vertical lines. The spatial-wise context feature can introduce the characteristic of layer structure to deep neural network. Finally, the original convolutional features are combined with connectedness feature according to the uncertainty degrees. The proposed methods are applied to two datasets: a BUS image benchmark with two categories (background and tumor) and a five-category BUS image dataset with fat layer, mammary layer, muscle layer, background, and tumor. The proposed method achieves the best results on both datasets compared with eight state-of-the-art deep learning-based approaches.

Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks

Sebastian Palacio, Philipp Engler, Jörn Hees, Andreas Dengel
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 16:30 in session PS T1.8

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Auto-TLDR; Self-Supervised Autogenous Learning for Deep Neural Networks

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Classification problems solved with deep neural networks (DNNs) typically rely on a closed world paradigm, and optimize over a single objective (e.g., minimization of the cross- entropy loss). This setup dismisses all kinds of supporting signals that can be used to reinforce the existence or absence of particular patterns. The increasing need for models that are interpretable by design makes the inclusion of said contextual signals a crucial necessity. To this end, we introduce the notion of Self-Supervised Autogenous Learning (SSAL). A SSAL objective is realized through one or more additional targets that are derived from the original supervised classification task, following architectural principles found in multi-task learning. SSAL branches impose low-level priors into the optimization process (e.g., grouping). The ability of using SSAL branches during inference, allow models to converge faster, focusing on a richer set of class-relevant features. We equip state-of-the-art DNNs with SSAL objectives and report consistent improvements for all of them on CIFAR100 and Imagenet. We show that SSAL models outperform similar state-of-the-art methods focused on contextual loss functions, auxiliary branches and hierarchical priors.

DA-RefineNet: Dual-Inputs Attention RefineNet for Whole Slide Image Segmentation

Ziqiang Li, Rentuo Tao, Qianrun Wu, Bin Li
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 12:00 in session PS T5.5

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Auto-TLDR; DA-RefineNet: A dual-inputs attention network for whole slide image segmentation

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Automatic medical image segmentation techniques have wide applications for disease diagnosing, however, its much more challenging than natural optical image segmentation tasks due to the high-resolution of medical images and the corresponding huge computation cost. Sliding window was a commonly used technique for whole slide image (WSI) segmentation, however, for these methods that based on sliding window, the main drawback was lacking of global contextual information for supervision. In this paper, we proposed a dual-inputs attention network (denoted as DA-RefineNet) for WSI segmentation, where both local fine-grained information and global coarse information can be efficiently utilized. Sufficient comparative experiments were conducted to evaluate the effectiveness of the proposed method, the results proved that the proposed method can achieve better performance on WSI segmentation tasks compared to methods rely on single-input.

RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery

Rohit Gupta, Mubarak Shah
Track 3: Computer Vision Robotics and Intelligent Systems
Wed 13 Jan 2021 at 16:30 in session PS T3.5

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Auto-TLDR; RescueNet: End-to-End Building Segmentation and Damage Classification for Humanitarian Aid and Disaster Response

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Accurate and fine-grained information about the extent of damage to buildings is essential for directing Humanitarian Aid and Disaster Response (HADR) operations in the immediate aftermath of any natural calamity. In recent years, satellite and UAV (drone) imagery has been used for this purpose, sometimes aided by computer vision algorithms. Existing Computer Vision approaches for building damage assessment typically rely on a two stage approach, consisting of building detection using an object detection model, followed by damage assessment through classification of the detected building tiles. These multi-stage methods are not end-to-end trainable, and suffer from poor overall results. We propose RescueNet, a unified model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-to end. In order to to model the composite nature of this problem, we propose a novel localization aware loss function, which consists of a Binary Cross Entropy loss for building segmentation, and a foreground only selective Categorical Cross-Entropy loss for damage classification, and show significant improvement over the widely used Cross-Entropy loss. RescueNet is tested on the large scale and diverse xBD dataset and achieves significantly better building segmentation and damage classification performance than previous methods and achieves generalization across varied geographical regions and disaster types.

Prediction of Obstructive Coronary Artery Disease from Myocardial Perfusion Scintigraphy using Deep Neural Networks

Ida Arvidsson, Niels Christian Overgaard, Miguel Ochoa Figueroa, Jeronimo Rose, Anette Davidsson, Kalle Åström, Anders Heyden
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 14:00 in session PS T1.6

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Auto-TLDR; A Deep Learning Algorithm for Multi-label Classification of Myocardial Perfusion Scintigraphy for Stable Ischemic Heart Disease

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For diagnosis and risk assessment in patients with stable ischemic heart disease, myocardial perfusion scintigraphy is one of the most common cardiological examinations performed today. There are however many motivations for why an artificial intelligence algorithm would provide useful input to this task. For example to reduce the subjectiveness and save time for the nuclear medicine physicians working with this time consuming task. In this work we have developed a deep learning algorithm for multi-label classification based on a modified convolutional neural network to estimate probability of obstructive coronary artery disease in the left anterior artery, left circumflex artery and right coronary artery. The prediction is based on data from myocardial perfusion scintigraphy studies conducted in a dedicated Cadmium-Zinc-Telluride cardio camera (D-SPECT Spectrum Dynamics). Data from 588 patients was available, with stress images in both upright and supine position, as well as a number of auxiliary parameters such as angina symptoms and BMI. The data was used to train and evaluate the algorithm using 5-fold cross-validation. We achieve state-of-the-art results for this task with an area under the receiver operating characteristics curve of 0.89 as average on per-vessel level and 0.94 on per-patient level.

MedZip: 3D Medical Images Lossless Compressor Using Recurrent Neural Network (LSTM)

Omniah Nagoor, Joss Whittle, Jingjing Deng, Benjamin Mora, Mark W. Jones
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 16:30 in session PS T5.4

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Auto-TLDR; Recurrent Neural Network for Lossless Medical Image Compression using Long Short-Term Memory

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As scanners produce higher-resolution and more densely sampled images, this raises the challenge of data storage, transmission and communication within healthcare systems. Since the quality of medical images plays a crucial role in diagnosis accuracy, medical imaging compression techniques are desired to reduce scan bitrate while guaranteeing lossless reconstruction. This paper presents a lossless compression method that integrates a Recurrent Neural Network (RNN) as a 3D sequence prediction model. The aim is to learn the long dependencies of the voxel's neighbourhood in 3D using Long Short-Term Memory (LSTM) network then compress the residual error using arithmetic coding. Experiential results reveal that our method obtains a higher compression ratio achieving 15% saving compared to the state-of-the-art lossless compression standards, including JPEG-LS, JPEG2000, JP3D, HEVC, and PPMd. Our evaluation demonstrates that the proposed method generalizes well to unseen modalities CT and MRI for the lossless compression scheme. To the best of our knowledge, this is the first lossless compression method that uses LSTM neural network for 16-bit volumetric medical image compression.

Tensor Factorization of Brain Structural Graph for Unsupervised Classification in Multiple Sclerosis

Berardino Barile, Marzullo Aldo, Claudio Stamile, Françoise Durand-Dubief, Dominique Sappey-Marinier
Track 3: Computer Vision Robotics and Intelligent Systems
Fri 15 Jan 2021 at 16:00 in session PS T3.11

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Auto-TLDR; A Fully Automated Tensor-based Algorithm for Multiple Sclerosis Classification based on Structural Connectivity Graph of the White Matter Network

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Analysis of longitudinal changes in brain diseases is essential for a better characterization of pathological processes and evaluation of the prognosis. This is particularly important in Multiple Sclerosis (MS) which is the first traumatic disease in young adults, with unknown etiology and characterized by complex inflammatory and degenerative processes leading to different clinical courses. In this work, we propose a fully automated tensor-based algorithm for the classification of MS clinical forms based on the structural connectivity graph of the white matter (WM) network. Using non-negative tensor factorization (NTF), we first focused on the detection of pathological patterns of the brain WM network affected by significant longitudinal variations. Second, we performed unsupervised classification of different MS phenotypes based on these longitudinal patterns, and finally, we used the latent factors obtained by the factorization algorithm to identify the most affected brain regions.

EM-Net: Deep Learning for Electron Microscopy Image Segmentation

Afshin Khadangi, Thomas Boudier, Vijay Rajagopal
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 12:00 in session PS T5.3

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Auto-TLDR; EM-net: Deep Convolutional Neural Network for Electron Microscopy Image Segmentation

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Recent high-throughput electron microscopy techniques such as focused ion-beam scanning electron microscopy (FIB-SEM) provide thousands of serial sections which assist the biologists in studying sub-cellular structures at high resolution and large volume. Low contrast of such images hinder image segmentation and 3D visualisation of these datasets. With recent advances in computer vision and deep learning, such datasets can be segmented and reconstructed in 3D with greater ease and speed than with previous approaches. However, these methods still rely on thousands of ground-truth samples for training and electron microscopy datasets require significant amounts of time for carefully curated manual annotations. We address these bottlenecks with EM-net, a scalable deep convolutional neural network for EM image segmentation. We have evaluated EM-net using two datasets, one of which belongs to an ongoing competition on EM stack segmentation since 2012. We show that EM-net variants achieve better performances than current deep learning methods using small- and medium-sized ground-truth datasets. We also show that the ensemble of top EM-net base classifiers outperforms other methods across a wide variety of evaluation metrics.

BG-Net: Boundary-Guided Network for Lung Segmentation on Clinical CT Images

Rui Xu, Yi Wang, Tiantian Liu, Xinchen Ye, Lin Lin, Yen-Wei Chen, Shoji Kido, Noriyuki Tomiyama
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 12:00 in session PS T5.3

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Auto-TLDR; Boundary-Guided Network for Lung Segmentation on CT Images

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Lung segmentation on CT images is a crucial step for a computer-aided diagnosis system of lung diseases. The existing deep learning based lung segmentation methods are less efficient to segment lungs on clinical CT images, especially that the segmentation on lung boundaries is not accurate enough due to complex pulmonary opacities in practical clinics. In this paper, we propose a boundary-guided network (BG-Net) to address this problem. It contains two auxiliary branches that separately segment lungs and extract the lung boundaries, and an aggregation branch that efficiently exploits lung boundary cues to guide the network for more accurate lung segmentation on clinical CT images. We evaluate the proposed method on a private dataset collected from the Osaka university hospital and four public datasets including StructSeg, HUG, VESSEL12, and a Novel Coronavirus 2019 (COVID-19) dataset. Experimental results show that the proposed method can segment lungs more accurately and outperform several other deep learning based methods.

BiLuNet: A Multi-Path Network for Semantic Segmentation on X-Ray Images

Van Luan Tran, Huei-Yung Lin, Rachel Liu, Chun-Han Tseng, Chun-Han Tseng
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 15:00 in session PS T5.7

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Auto-TLDR; BiLuNet: Multi-path Convolutional Neural Network for Semantic Segmentation of Lumbar vertebrae, sacrum,

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Semantic segmentation and shape detection of lumbar vertebrae, sacrum, and femoral heads from clinical X-ray images are important and challenging tasks. In this paper, we propose a new multi-path convolutional neural network, BiLuNet, for semantic segmentation on X-ray images. The network is capable of medical image segmentation with very limited training data. With the shape fitting of the bones, we can identify the location of the target regions very accurately for lumbar vertebra inspection. We collected our dataset and annotated by doctors for model training and performance evaluation. Compared to the state-of-the-art methods, the proposed technique provides better mIoUs and higher success rates with the same training data. The experimental results have demonstrated the feasibility of our network to perform semantic segmentation for lumbar vertebrae, sacrum, and femoral heads.

CAggNet: Crossing Aggregation Network for Medical Image Segmentation

Xu Cao, Yanghao Lin
Track 3: Computer Vision Robotics and Intelligent Systems
Wed 13 Jan 2021 at 12:00 in session PS T3.4

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Auto-TLDR; Crossing Aggregation Network for Medical Image Segmentation

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In this paper, we present Crossing Aggregation Network (CAggNet), a novel densely connected semantic segmentation method for medical image analysis. The crossing aggregation network absorbs the idea of deep layer aggregation and makes significant innovations in layer connection and semantic information fusion. In this architecture, the traditional skip-connection structure of general U-Net is replaced by aggregations of multi-level down-sampling and up-sampling layers. This enables the network to fuse information interactively flows at different levels of layers in semantic segmentation. It also introduces weighted aggregation module to aggregate multi-scale output information. We have evaluated and compared our CAggNet with several advanced U-Net based methods in two public medical image datasets, including the 2018 Data Science Bowl nuclei detection dataset and the 2015 MICCAI gland segmentation competition dataset. Experimental results indicate that CAggNet improves medical object recognition and achieves a more accurate and efficient segmentation compared to existing improved U-Net and UNet++ structure.

Vesselness Filters: A Survey with Benchmarks Applied to Liver Imaging

Jonas Lamy, Odyssée Merveille, Bertrand Kerautret, Nicolas Passat, Antoine Vacavant
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 16:00 in session PS T5.6

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Auto-TLDR; Comparison of Vessel Enhancement Filters for Liver Vascular Network Segmentation

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The accurate knowledge of vascular network geometry is crucial for many clinical applications such as cardiovascular disease diagnosis and surgery planning. Vessel enhancement algorithms are often a key step to improve the robustness of vessel segmentation. A wide variety of enhancement filters exists in the literature, but they are often difficult to compare as the applications and datasets differ from a paper to another and the code is rarely available. In this article, we compare seven vessel enhancement filters covering the last twenty years literature in a unique common framework. We focus our study on the liver vascular network which is under-represented in the literature. The evaluation is made from three points of view: in the whole liver, in the vessel neighborhood and near the bifurcations. The study is performed on two publicly available datasets: the Ircad dataset (CT images) and the VascuSynth dataset adapted for MRI simulation. We discuss the strengths and weaknesses of each method in the hepatic context. In addition, the benchmark framework including a C++ implementation of each compared method is provided. An online demonstration ensures the reproducibility of the results without requiring any additional software.