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Transfer Learning through Weighted Loss Function and Group Normalization for Vessel Segmentation from Retinal Images

Abdullah Sarhan, Jon Rokne, Reda Alhajj, Andrew Crichton

Auto-TLDR; Deep Learning for Segmentation of Blood Vessels in Retinal Images

Abstract Slides Poster

The vascular structure of blood vessels is important in diagnosing retinal conditions such as glaucoma and diabetic retinopathy. Accurate segmentation of these vessels can help in detecting retinal objects such as the optic disc and optic cup and hence determine if there are damages to these areas. Moreover, the structure of the vessels can help in diagnosing glaucoma. The rapid development of digital imaging and computer-vision techniques has increased the potential for developing approaches for segmenting retinal vessels. In this paper, we propose an approach for segmenting retinal vessels that uses deep learning along with transfer learning. We adapted the U-Net structure to use a customized InceptionV3 as the encoder and used multiple skip connections to form the decoder. Moreover, we used a weighted loss function to handle the issue of class imbalance in retinal images. Furthermore, we contributed a new dataset to this field. We tested our approach on six publicly available datasets and a newly created dataset. We achieved an average accuracy of 95.60\% and a Dice coefficient of 80.98\%. The results obtained from comprehensive experiments demonstrate the robustness of our approach to the segmentation of blood vessels in retinal images obtained from different sources. Our approach results in greater segmentation accuracy than other approaches.

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SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation

Changlu Guo, Marton Szemenyei, Yugen Yi, Wenle Wang, Buer Chen, Changqi Fan

Auto-TLDR; Spatial Attention U-Net for Segmentation of Retinal Blood Vessels

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The precise segmentation of retinal blood vessels is of great significance for early diagnosis of eye-related diseases such as diabetes and hypertension. In this work, we propose a lightweight network named Spatial Attention U-Net (SA-UNet) that does not require thousands of annotated training samples and can be utilized in a data augmentation manner to use the available annotated samples more efficiently. SA-UNet introduces a spatial attention module which infers the attention map along the spatial dimension, and multiplies the attention map by the input feature map for adaptive feature refinement. In addition, the proposed network employs structured dropout convolutional blocks instead of the original convolutional blocks of U-Net to prevent the network from overfitting. We evaluate SA-UNet based on two benchmark retinal datasets: the Vascular Extraction (DRIVE) dataset and the Child Heart and Health Study (CHASE_DB1) dataset. The results show that the proposed SA-UNet achieves state-of-the-art performance on both datasets.The implementation and the trained networks are available on Github1.

PCANet: Pyramid Context-Aware Network for Retinal Vessel Segmentation

Yi Zhang, Yixuan Chen, Kai Zhang

Auto-TLDR; PCANet: Adaptive Context-Aware Network for Automated Retinal Vessel Segmentation

Abstract Slides Poster Similar

Automated retinal vessel segmentation plays an important role in the diagnosis of some diseases such as diabetes, arteriosclerosis and hypertension. Recent works attempt to improve segmentation performance by exploring either global or local contexts. However, the context demands are varying from regions in each image and different levels of network. To address these problems, we propose Pyramid Context-aware Network (PCANet), which can adaptively capture multi-scale context representations. Specifically, PCANet is composed of multiple Adaptive Context-aware (ACA) blocks arranged in parallel, each of which can adaptively obtain the context-aware features by estimating affinity coefficients at a specific scale under the guidance of global contextual dependencies. Meanwhile, we import ACA blocks with specific scales in different levels of the network to obtain a coarse-to-fine result. Furthermore, an integrated test-time augmentation method is developed to further boost the performance of PCANet. Finally, extensive experiments demonstrate the effectiveness of the proposed PCANet, and state-of-the-art performances are achieved with AUCs of 0.9866, 0.9886 and F1 Scores of 0.8274, 0.8371 on two public datasets, DRIVE and STARE, respectively.

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

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.

NephCNN: A Deep-Learning Framework for Vessel Segmentation in Nephrectomy Laparoscopic Videos

Alessandro Casella, Sara Moccia, Chiara Carlini, Emanuele Frontoni, Elena De Momi, Leonardo Mattos

Auto-TLDR; Adversarial Fully Convolutional Neural Networks for kidney vessel segmentation from nephrectomy laparoscopic videos

Abstract Slides Poster Similar

Objective: In the last years, Robot-assisted partial nephrectomy (RAPN) is establishing as elected treatment for renal cell carcinoma (RCC). Reduced field of view, field occlusions by surgical tools, and reduced maneuverability may potentially cause accidents, such as unwanted vessel resection with consequent bleeding. Surgical Data Science (SDS) can provide effective context-aware tools for supporting surgeons. However, currently no tools have been exploited for automatic vessels segmentation from nephrectomy laparoscopic videos. Herein, we propose a new approach based on adversarial Fully Convolutional Neural Networks (FCNNs) to kidney vessel segmentation from nephrectomy laparoscopic vision. Methods: The proposed approach enhances existing segmentation framework by (i) encoding 3D kernels for spatio-temporal features extraction to enforce pixel connectivity in time, and (ii) perform training in adversarial fashion, which constrains vessels shape. Results: We performed a preliminary study using 8 different RAPN videos (1871 frames), the first in the field, achieving a median Dice Similarity Coefficient of 71.76%. Conclusions: Results showed that the proposed approach could be a valuable solution with a view to assist surgeon during RAPN.

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

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

Abstract Poster Similar

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.

Progressive Adversarial Semantic Segmentation

Abdullah-Al-Zubaer Imran, Demetri Terzopoulos

Auto-TLDR; Progressive Adversarial Semantic Segmentation for End-to-End Medical Image Segmenting

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Medical image computing has advanced rapidly with the advent of deep learning techniques such as convolutional neural networks. Deep convolutional neural networks can perform exceedingly well given full supervision. However, the success of such fully-supervised models for various image analysis tasks (e.g., anatomy or lesion segmentation from medical images) is limited to the availability of massive amounts of labeled data. Given small sample sizes, such models are prohibitively data biased with large domain shift. To tackle this problem, we propose a novel end-to-end medical image segmentation model, namely Progressive Adversarial Semantic Segmentation (PASS), which can make improved segmentation predictions without requiring any domain-specific data during training time. Our extensive experimentation with 8 public diabetic retinopathy and chest X-ray datasets, confirms the effectiveness of PASS for accurate vascular and pulmonary segmentation, both for in-domain and cross-domain evaluations.

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

Subhashis Banerjee, Ashis Kumar Dhara, Johan Wikström, Robin Strand

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 $0.73\pm0.06$.

Fine-Tuning Convolutional Neural Networks: A Comprehensive Guide and Benchmark Analysis for Glaucoma Screening

Amed Mvoulana, Rostom Kachouri, Mohamed Akil

Auto-TLDR; Fine-tuning Convolutional Neural Networks for Glaucoma Screening

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This work aimed at giving a comprehensive and in-detailed guide on the route to fine-tuning Convolutional Neural Networks (CNNs) for glaucoma screening. Transfer learning consists in a promising alternative to train CNNs from stratch, to avoid the huge data and resources requirements. After a thorough study of five state-of-the-art CNNs architectures, a complete and well-explained strategy for fine-tuning these networks is proposed, using hyperparameter grid-searching and two-phase training approach. Excellent performance is reached on model evaluation, with a 0.9772 AUROC validation rate, giving arise to reliable glaucoma diagosis-help systems. Also, a benchmark analysis is conducted across all fine-tuned models, studying them according to performance indices such as model complexity and size, AUROC density and inference time. This in-depth analysis allows a rigorous comparison between model characteristics, and is useful for giving practioners important trademarks for prospective applications and deployments.

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

Martin Kolarik, Radim Burget, Carlos M. Travieso-Gonzalez, Jan Kocica

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.

Few Shot Learning Framework to Reduce Inter-Observer Variability in Medical Images

Sohini Roychowdhury

Auto-TLDR; Few-Shot Learning for Quality Image Annotation

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Most computer aided pathology detection systems rely on large volumes of quality annotated data to aid diagnostics and follow up procedures. However, quality assuring large volumes of annotated medical image data can be subjective and expensive. In this work we present a novel standardization framework that implements three few-shot learning (FSL) models that can be iteratively trained by atmost 5 images per 3D stack to generate multiple regional proposals (RPs) per test image. These FSL models include a novel parallel echo state network framework and an augmented U-net model. Additionally, we propose a novel target label selection algorithm (TLSA) that measures relative agreeability between RPs and the manually annotated target labels to detect the ``best" quality annotation per image. Using the FSL models, our system achieves 0.28-0.64 Dice coefficient across vendor image stacks for intra-retinal cyst segmentation. Additionally, the TLSA is capable of correctly classifying high quality target labels from their noisy counterparts with 70-97% accuracy. The proposed system significantly automates high quality annotation selection on an image level while minimizing manual quality checking to 12-28% of the images only. Thus, the proposed framework is flexible to extensions for quality image annotation curation of other image stacks as well.

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

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.

CAggNet: Crossing Aggregation Network for Medical Image Segmentation

Xu Cao, Yanghao Lin

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.

A Deep Learning Approach for the Segmentation of Myocardial Diseases

Khawala Brahim, Abdull Qayyum, Alain Lalande, Arnaud Boucher, Anis Sakly, Fabrice Meriaudeau

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.

Robust Localization of Retinal Lesions Via Weakly-Supervised Learning

Ruohan Zhao, Qin Li, Jane You

Auto-TLDR; Weakly Learning of Lesions in Fundus Images Using Multi-level Feature Maps and Classification Score

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Retinal fundus images reveal the condition of retina, blood vessels and optic nerve. Retinal imaging is becoming widely adopted in clinical work because any subtle changes to the structures at the back of the eyes can affect the eyes and indicate the overall health. Machine learning, in particular deep learning by convolutional neural network (CNN), has been increasingly adopted for computer-aided detection (CAD) of retinal lesions. However, a significant barrier to the high performance of CNN based CAD approach is caused by the lack of sufficient labeled ground-truth image samples for training. Unlike the fully-supervised learning which relies on pixel-level annotation of pathology in fundus images, this paper presents a new approach to discriminate the location of various lesions based on image-level labels via weakly learning. More specifically, our proposed method leverages multi-level feature maps and classification score to cope with both bright and red lesions in fundus images. To enhance capability of learning less discriminative parts of objects (e.g. small blobs of microaneurysms opposed to bulk of exudates), the classifier is regularized by refining images with corresponding labels. The experimental results of the performance evaluation and benchmarking at both image-level and pixel-level on the public DIARETDB1 dataset demonstrate the feasibility and excellent potentials of our method in practice.

DE-Net: Dilated Encoder Network for Automated Tongue Segmentation

Hui Tang, Bin Wang, Jun Zhou, Yongsheng Gao

Auto-TLDR; Automated Tongue Image Segmentation using De-Net

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Automated tongue recognition is a growing research field due to global demand for personal health care. Using mobile devices to take tongue pictures is convenient and of low cost for tongue recognition. It is particularly suitable for self-health evaluation of the public. However, images taken by mobile devices are easily affected by various imaging environment, which makes fine segmentation a more challenging task compared with those taken by specialized acquisition devices. Deep learning approaches are promising for tongue image segmentation because they have powerful feature learning and representation capability. However, the successive pooling operations in these methods lead to loss of information on image details, making them fail when segmenting low-quality images captured by mobile devices. To address this issue, we propose a dilated encoder network (DE-Net) to capture more high-level features and get high-resolution output for automated tongue image segmentation. In addition, we construct two tongue image datasets which contain images taken by specialized devices and mobile devices, respectively, to verify the effectiveness of the proposed method. Experimental results on both datasets demonstrate that the proposed method outperforms the state-of-the-art methods in tongue image segmentation.

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

Yongpei Zhu, Zicong Zhou, Guojun Liao, Kehong Yuan

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.

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

Zhou Zhao, Elodie Puybareau, Nicolas Boutry, Thierry Geraud

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

Abstract Slides Poster Similar

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.

Attention2AngioGAN: Synthesizing Fluorescein Angiography from Retinal Fundus Images Using Generative Adversarial Networks

Sharif Amit Kamran, Khondker Fariha Hossain, Alireza Tavakkoli, Stewart Lee Zuckerbrod

Auto-TLDR; Fluorescein Angiography from Fundus Images using Attention-based Generative Networks

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Fluorescein Angiography (FA) is a technique that employs the designated camera for Fundus photography incorporating excitation and barrier filters. FA also requires fluorescein dye that is injected intravenously, which might cause adverse effects ranging from nausea, vomiting to even fatal anaphylaxis. Currently, no other fast and non-invasive technique exists that can generate FA without coupling with Fundus photography. To eradicate the need for an invasive FA extraction procedure, we introduce an Attention-based Generative network that can synthesize Fluorescein Angiography from Fundus images. The proposed gan incorporates multiple attention based skip connections in generators and comprises novel residual blocks for both generators and discriminators. It utilizes reconstruction, feature-matching, and perceptual loss along with adversarial training to produces realistic Angiograms that is hard for experts to distinguish from real ones. Our experiments confirm that the proposed architecture surpasses recent state-of-the-art generative networks for fundus-to-angio translation task.

Segmenting Kidney on Multiple Phase CT Images Using ULBNet

Yanling Chi, Yuyu Xu, Gang Feng, Jiawei Mao, Sihua Wu, Guibin Xu, Weimin Huang

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 Systematic Investigation on Deep Architectures for Automatic Skin Lesions Classification

Pierluigi Carcagni, Marco Leo, Andrea Cuna, Giuseppe Celeste, Cosimo Distante

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.

Vesselness Filters: A Survey with Benchmarks Applied to Liver Imaging

Jonas Lamy, Odyssée Merveille, Bertrand Kerautret, Nicolas Passat, Antoine Vacavant

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.

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

Wei Chen, Qiuli Wang, Dan Yang, Xiaohong Zhang, Chen Liu, Yucong Li

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.

Multi-focus Image Fusion for Confocal Microscopy Using U-Net Regression Map

Md Maruf Hossain Shuvo, Yasmin M. Kassim, Filiz Bunyak, Olga V. Glinskii, Leike Xie, Vladislav V Glinsky, Virginia H. Huxley, Kannappan Palaniappan

Auto-TLDR; Independent Single Channel U-Net Fusion for Multi-focus Microscopy Images

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Multi-focus image fusion plays an important role to better visualize the detailed information and anatomical structures of microscopy images. We propose a new approach to fuse all single-focus microscopy images in each Z-stack. As the structures are different in different channels, input images are separated into red and green channels. Red for blood vessels, and green for lymphatics like structures . Taking the maximum likelihood of U-Net regression likelihood map along Z, we obtain the focus selection map for each channel. We named this approach as Independent Single Channel U-Net (ISCU) fusion. We combined each channel fusion result to get the final dual channel composite RGB image. The dataset used is extremely challenging with complex microscopy images of mice dura mater attached to bone. We compared our results with one of the popular and widely used derivative based fusion method [7] using multiscale Hessian. We found that multiscale Hessian-based approach produces banding effects with nonhomogeneous background lacking detailed anatomical structures. So, we took the advantages of Convolutional Neural Network (CNN), and used the U-Net regression likelihood map to fuse the images. Perception based no-reference image quality assessment parameters like PIQUE, NIQE, and BRISQUE confirms the effectiveness of the proposed method.

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

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.

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

Zhou Zhao, Elodie Puybareau, Nicolas Boutry, Thierry Geraud

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.

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

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.

Aerial Road Segmentation in the Presence of Topological Label Noise

Corentin Henry, Friedrich Fraundorfer, Eleonora Vig

Auto-TLDR; Improving Road Segmentation with Noise-Aware U-Nets for Fine-Grained Topology delineation

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The availability of large-scale annotated datasets has enabled Fully-Convolutional Neural Networks to reach outstanding performance on road extraction in aerial images. However, high-quality pixel-level annotation is expensive to produce and even manually labeled data often contains topological errors. Trading off quality for quantity, many datasets rely on already available yet noisy labels, for example from OpenStreetMap. In this paper, we explore the training of custom U-Nets built with ResNet and DenseNet backbones using noise-aware losses that are robust towards label omission and registration noise. We perform an extensive evaluation of standard and noise-aware losses, including a novel Bootstrapped DICE-Coefficient loss, on two challenging road segmentation benchmarks. Our losses yield a consistent improvement in overall extraction quality and exhibit a strong capacity to cope with severe label noise. Our method generalizes well to two other fine-grained topology delineation tasks: surface crack detection for quality inspection and cell membrane extraction in electron microscopy imagery.

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

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.

Offset Curves Loss for Imbalanced Problem in Medical Segmentation

Ngan Le, Duc Toan Bui, Khoa Luu, Marios Savvides

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.

Triplet-Path Dilated Network for Detection and Segmentation of General Pathological Images

Jiaqi Luo, Zhicheng Zhao, Fei Su, Limei Guo

Auto-TLDR; Triplet-path Network for One-Stage Object Detection and Segmentation in Pathological Images

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Deep learning has been widely applied in the field of medical image processing. However, compared with flourishing visual tasks in natural images, the progress achieved in pathological images is not remarkable, and detection and segmentation, which are among basic tasks of computer vision, are regarded as two independent tasks. In this paper, we make full use of existing datasets and construct a triplet-path network using dilated convolutions to cooperatively accomplish one-stage object detection and nuclei segmentation for general pathological images. First, in order to meet the requirement of detection and segmentation, a novel structure called triplet feature generation (TFG) is designed to extract high-resolution and multiscale features, where features from different layers can be properly integrated. Second, considering that pathological datasets are usually small, a location-aware and partially truncated loss function is proposed to improve the classification accuracy of datasets with few images and widely varying targets. We compare the performance of both object detection and instance segmentation with state-of-the-art methods. Experimental results demonstrate the effectiveness and efficiency of the proposed network on two datasets collected from multiple organs.

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

Ngan Le, Kashu Yamazaki, Quach Kha Gia, Thanh-Dat Truong, Marios Savvides

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.

A Comparison of Neural Network Approaches for Melanoma Classification

Maria Frasca, Michele Nappi, Michele Risi, Genoveffa Tortora, Alessia Auriemma Citarella

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.

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

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.

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

Ziqiang Li, Rentuo Tao, Qianrun Wu, Bin Li

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.

Motion U-Net: Multi-Cue Encoder-Decoder Network for Motion Segmentation

Gani Rahmon, Filiz Bunyak, Kannappan Palaniappan

Auto-TLDR; Motion U-Net: A Deep Learning Framework for Robust Moving Object Detection under Challenging Conditions

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Detection of moving objects is a critical first step in many computer vision applications. Several algorithms for motion and change detection were proposed. However, many of these approaches lack the ability to handle challenging real-world scenarios. Recently, deep learning approaches started to produce impressive solutions to computer vision tasks, particularly for detection and segmentation. Many existing deep learning networks proposed for moving object detection rely only on spatial appearance cues. In this paper, we propose a novel multi-cue and multi-stream network, Motion U-Net (MU-Net), which integrates motion, change, and appearance cues using a deep learning framework for robust moving object detection under challenging conditions. The proposed network consists of a two-stream encoder module followed by feature concatenation and a decoder module. Motion and change cues are computed through our tensor-based motion estimation and a multi-modal background subtraction modules. The proposed system was tested and evaluated on the change detection challenge datasets (CDnet-2014) and compared to state-of-the-art methods. On CDnet-2014 dataset, our approach reaches an average overall F-measure of 0.9852 and outperforms all current state-of-the-art methods. The network was also tested on the unseen SBI-2015 dataset and produced promising results.

OCT Image Segmentation Using NeuralArchitecture Search and SRGAN

Saba Heidari, Omid Dehzangi, Nasser M. Nasarabadi, Ali Rezai

Auto-TLDR; Automatic Segmentation of Retinal Layers in Optical Coherence Tomography using Neural Architecture Search

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Alzheimer’s disease (AD) diagnosis is one of the major research areas in computational medicine. Optical coherence tomography (OCT) is a non-invasive, inexpensive, and timely efficient method that scans the human’s retina with depth. It has been hypothesized that the thickness of the retinal layers extracted from OCTs could be an efficient and effective biomarker for early diagnosis of AD. In this work, we aim to design a self-training model architecture for the task of segmenting the retinal layers in OCT scans. Neural architecture search (NAS) is a subfield of AutoML domain, which has a significant impact on improving the accuracy of machine vision tasks. We integrate the NAS algorithm with a Unet auto-encoder architecture as its backbone. Then, we employ our proposed model to segment the retinal nerve fiber layer in our preprocessed OCT images with the aim of AD diagnosis. In this work, we trained a super-resolution generative adversarial network on the raw OCT scans to improve the quality of the images before the modeling stage. In our architecture search strategy, different primitive operations suggested to find down- \& up-sampling Unet cell blocks and the binary gate method has been applied to make the search strategy more practical. Our architecture search method is empirically evaluated by training on the Unet and NAS-Unet from scratch. Specifically, the proposed NAS-Unet training significantly outperforms the baseline human-designed architecture by achieving 95.1\% in the mean Intersection over Union metric and 79.1\% in the Dice similarity coefﬁcient.

Dual Encoder Fusion U-Net (DEFU-Net) for Cross-manufacturer Chest X-Ray Segmentation

Zhang Lipei, Aozhi Liu, Jing Xiao

Auto-TLDR; Inception Convolutional Neural Network with Dilation for Chest X-Ray Segmentation

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A number of methods based on the deep learning have been applied to medical image segmentation and have achieved state-of-the-art performance. The most famous technique is U-Net which has been used to many medical datasets including the Chest X-ray. Due to the importance of chest x- ray data in studying COVID-19, there is a demand for state-of- art models capable of precisely segmenting chest x-rays. In this paper, we propose a dual encoder fusion U-Net framework for Chest X-rays based on Inception Convolutional Neural Network with dilation, Densely Connected Recurrent Convolutional Neural Network, which is named DEFU-Net. The densely connected recurrent path extends the network deeper for facilitating context feature extraction. In order to increase the width of network and enrich representation of features, the inception blocks with dilation have been used. The inception blocks can capture globally and locally spatial information with various receptive fields to avoid information loss caused by max-pooling. Meanwhile, the features fusion of two path by summation preserve the context and the spatial information for decoding part. We applied this model in Chest X-ray dataset from two different manufacturers (Montgomery and Shenzhen hospital). The DEFU-Net achieves the better performance than basic U-Net, residual U-Net, BCDU- Net, R2U-Net and attention R2U-Net. This model approaches state-of-the-art in this mixed dataset. The open source code for this proposed framework is public available.

Detection and Correspondence Matching of Corneal Reflections for Eye Tracking Using Deep Learning

Soumil Chugh, Braiden Brousseau, Jonathan Rose, Moshe Eizenman

Auto-TLDR; A Fully Convolutional Neural Network for Corneal Reflection Detection and Matching in Extended Reality Eye Tracking Systems

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Eye tracking systems that estimate the point-of-gaze are essential in extended reality (XR) systems as they enable new interaction paradigms and technological improvements. It is important for these systems to maintain accuracy when the headset moves relative to the head (known as device slippage) due to head movements or user adjustment. One of the most accurate eye tracking techniques, which is also insensitive to shifts of the system relative to the head, uses two or more infrared (IR) light emitting diodes to illuminate the eye and an IR camera to capture images of the eye. An essential step in estimating the point-of-gaze in these systems is the precise determination of the location of two or more corneal reflections (virtual images of the IR-LEDs that illuminate the eye) in images of the eye. Eye trackers tend to have multiple light sources to ensure at least one pair of reflections for each gaze position. The use of multiple light sources introduces a difficult problem: the need to match the corneal reflections with the corresponding light source over the range of expected eye movements. Corneal reflection detection and matching often fail in XR systems due to the proximity of camera and steep illumination angles of light sources with respect to the eye. The failures are caused by corneal reflections having varying shape and intensity levels or disappearance due to rotation of the eye, or the presence of spurious reflections. We have developed a fully convolutional neural network, based on the UNET architecture, that solves the detection and matching problem in the presence of spurious and missing reflections. Eye images of 25 people were collected in a virtual reality headset using a binocular eye tracking module consisting of five infrared light sources per eye. A set of 4,000 eye images were manually labelled for each of the corneal reflections, and data augmentation was used to generate a dataset of 40,000 images. The network is able to correctly identify and match 91% of corneal reflections present in the test set. This is comparable to a state-of-the-art deep learning system, but our approach requires 33 times less memory and executes 10 times faster. The proposed algorithm, when used in an eye tracker in a VR system, achieved an average mean absolute gaze error of 1°. This is a significant improvement over the state-of-the-art learning-based XR eye tracking systems that have reported gaze errors of 2-3°.

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

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.

Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval

Stefano Allegretti, Federico Bolelli, Federico Pollastri, Sabrina Longhitano, Giovanni Pellacani, Costantino Grana

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.

RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery

Rohit Gupta, Mubarak Shah

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.

EM-Net: Deep Learning for Electron Microscopy Image Segmentation

Afshin Khadangi, Thomas Boudier, Vijay Rajagopal

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.

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

Krishnam Gupta, Jaiprasad Rampure, Monu Krishnan, Ajit Narayanan, Nikhil Narayan

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.

CT-UNet: An Improved Neural Network Based on U-Net for Building Segmentation in Remote Sensing Images

Huanran Ye, Sheng Liu, Kun Jin, Haohao Cheng

Auto-TLDR; Context-Transfer-UNet: A UNet-based Network for Building Segmentation in Remote Sensing Images

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With the proliferation of remote sensing images, how to segment buildings more accurately in remote sensing images is a critical challenge. First, the high resolution leads to blurred boundaries in the extracted building maps. Second, the similarity between buildings and background results in intra-class inconsistency. To address these two problems, we propose an UNet-based network named Context-Transfer-UNet (CT-UNet). Specifically, we design Dense Boundary Block (DBB). Dense Block utilizes reuse mechanism to refine features and increase recognition capabilities. Boundary Block introduces the low-level spatial information to solve the fuzzy boundary problem. Then, to handle intra-class inconsistency, we construct Spatial Channel Attention Block (SCAB). It combines context space information and selects more distinguishable features from space and channel. Finally, we propose a novel loss function to enhance the purpose of loss by adding evaluation indicator. Based on our proposed CT-UNet, we achieve 85.33% mean IoU on the Inria dataset and 91.00% mean IoU on the WHU dataset, which outperforms our baseline (U-Net ResNet-34) by 3.76% and Web-Net by 2.24%.

Dealing with Scarce Labelled Data: Semi-Supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-Ray Images

Saúl Calderón Ramirez, Raghvendra Giri, Shengxiang Yang, Armaghan Moemeni, Mario Umaña, David Elizondo, Jordina Torrents-Barrena, Miguel A. Molina-Cabello

Auto-TLDR; Semi-supervised Deep Learning for Covid-19 Detection using Chest X-rays

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Coronavirus (Covid-19) is spreading fast, infecting people through contact in various forms including droplets from sneezing and coughing. Therefore, the detection of infected subjects in an early, quick and cheap manner is urgent. Currently available tests are scarce and limited to people in danger of serious illness. The application of deep learning to chest X-ray images for Covid-19 detection is an attractive approach. However, this technology usually relies on the availability of large labelled datasets, a requirement hard to meet in the context of a virus outbreak. To overcome this challenge, a semi-supervised deep learning model using both labelled and unlabelled data is proposed. We developed and tested a semi-supervised deep learning framework based on the Mix Match architecture to classify chest X-rays into Covid-19, pneumonia and healthy cases. The presented approach was calibrated using two publicly available datasets. The results show an accuracy increase of around $15\%$ under low labelled / unlabelled data ratio. This indicates that our semi-supervised framework can help improve performance levels towards Covid-19 detection when the amount of high-quality labelled data is scarce. Also, we introduce a semi-supervised deep learning boost coefficient which is meant to ease the scalability of our approach and performance comparison.

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

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.

Investigating and Exploiting Image Resolution for Transfer Learning-Based Skin Lesion Classification

Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Rupert Ecker, Georg Dorffner, Isabella Ellinger

Auto-TLDR; Fine-tuned Neural Networks for Skin Lesion Classification Using Dermoscopic Images

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Skin cancer is among the most common cancer types. Dermoscopic image analysis improves the diagnostic accuracy for detection of malignant melanoma and other pigmented skin lesions when compared to unaided visual inspection. Hence, computer-based methods to support medical experts in the diagnostic procedure are of great interest. Fine-tuning pre-trained convolutional neural networks (CNNs) has been shown to work well for skin lesion classification. Pre-trained CNNs are usually trained with natural images of a fixed image size which is typically significantly smaller than captured skin lesion images and consequently dermoscopic images are downsampled for fine-tuning. However, useful medical information may be lost during this transformation. In this paper, we explore the effect of input image size on skin lesion classification performance of fine-tuned CNNs. For this, we resize dermoscopic images to different resolutions, ranging from 64x64 to 768x768 pixels and investigate the resulting classification performance of three well-established CNNs, namely DenseNet-121, ResNet-18, and ResNet-50. Our results show that using very small images (of size 64x64 pixels) degrades the classification performance, while images of size 128x128 pixels and above support good performance with larger image sizes leading to slightly improved classification. We further propose a novel fusion approach based on a three-level ensemble strategy that exploits multiple fine-tuned networks trained with dermoscopic images at various sizes. When applied on the ISIC 2017 skin lesion classification challenge, our fusion approach yields an area under the receiver operating characteristic curve of 89.2% and 96.6% for melanoma classification and seborrheic keratosis classification, respectively, outperforming state-of-the-art algorithms.

Fused 3-Stage Image Segmentation for Pleural Effusion Cell Clusters

Sike Ma, Meng Zhao, Hao Wang, Fan Shi, Xuguo Sun, Shengyong Chen, Hong-Ning Dai

Auto-TLDR; Coarse Segmentation of Stained and Stained Unstained Cell Clusters in pleural effusion using 3-stage segmentation method

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The appearance of tumor cell clusters in pleural effusion is usually a vital sign of cancer metastasis. Segmentation, as an indispensable basis, is of crucial importance for diagnosing, chemical treatment, and prognosis in patients. However, accurate segmentation of unstained cell clusters containing more detailed features than the fluorescent staining images remains to be a challenging problem due to the complex background and the unclear boundary. Therefore, in this paper, we propose a fused 3-stage image segmentation algorithm, namely Coarse segmentation-Mapping-Fine segmentation (CMF) to achieve unstained cell clusters from whole slide images. Firstly, we establish a tumor cell cluster dataset consisting of 107 sets of images, with each set containing one unstained image, one stained image, and one ground-truth image. Then, according to the features of the unstained and stained cell clusters, we propose a three-stage segmentation method: 1) Coarse segmentation on stained images to extract suspicious cell regions-Region of Interest (ROI); 2) Mapping this ROI to the corresponding unstained image to get the ROI of the unstained image (UI-ROI); 3) Fine Segmentation using improved automatic fuzzy clustering framework (AFCF) on the UI-ROI to get precise cell cluster boundaries. Experimental results on 107 sets of images demonstrate that the proposed algorithm can achieve better performance on unstained cell clusters with an F1 score of 90.40%.