Eugenia Moris, Nicolás Dazeo, Maria Paula Albina de Rueda, Francisco Filizzola, Nicolás Iannuzzo, Danila Nejamkin, Kevin Wignall, Mercedes Leguía, Ignacio Larra… 展开作者
Eugenia Moris, Nicolás Dazeo, Maria Paula Albina de Rueda, Francisco Filizzola, Nicolás Iannuzzo, Danila Nejamkin, Kevin Wignall, Mercedes Leguía, Ignacio Larrabide, José Ignacio Orlando
发表信息：
18th International Symposium on Medical Information Processing and Analysis (SIPAIM) 2022
Automated optic disc (OD) and optic cup (OC) segmentation in fundus images is relevant to efficiently measure the vertical cup-to-disc ratio (vCDR), a biomarker commonly used in ophthalmology to determine the degree of glaucomatous optic neuropathy. In general this is solved using coarse-to-fine deep learning algorithms in which a first stage approximates the OD and a second one uses a crop of this area to predict OD/OC masks. While this approach is widely applied in the literature, there are no studies analyzing its real contribution to the results. In this paper we present a comprehensive analysis of different coarse-to-fine designs for OD/OC segmentation using 5 public databases, both from a standard segmentation perspective and for estimating the vCDR for glaucoma assessment. Our analysis shows that these algorithms not necessarily outperfom standard multi-class single-stage models, especially when these are learned from sufficiently large and diverse training sets. Furthermore, we noticed that the coarse stage achieves better OD segmentation results than the fine one, and that providing OD supervision to the second stage is essential to ensure accurate OC masks. Moreover, both the single-stage and two-stage models trained on a multi-dataset setting showed results in pair or even better than other state-of-the-art alternatives, while ranking first in REFUGE for OD/OC segmentation. Finally, we evaluated the models for vCDR prediction in comparison with six ophthalmologists on a subset of AIROGS images, to understand them in the context of inter-observer variability. We noticed that vCDR estimates recovered both from single-stage and coarse-to-fine models can obtain good glaucoma detection results even when they are not highly correlated with manual measurements from experts.
In this manuscript, we present a robust method for glaucoma screening from fundus images using an ensemble of convolutional neural networks (CNNs). The pipeline comprises of first segmenting the optic disk and optic cup from the fundus image, then extracting a patch centered around the optic disk and subsequently feeding to the classification network to differentiate the image as diseased or healthy. In the segmentation network, apart from the image, we make use of spatial co-ordinate (X \& Y) space so as to learn the structure of interest better. The classification network is composed of a De…
Universitat Rovira i Virgili（罗维拉-威尔吉利大学）；Bioinformatics Institute（生物信息学研究所）
In this paper, an optic disc and cup segmentation method is proposed using U-Net followed by a multi-scale feature matching network. The proposed method targets task 2 of the REFUGE challenge 2018. In order to solve the segmentation problem of task 2, we firstly crop the input image using single shot multibox detector (SSD). The cropped image is then passed to an encoder-decoder network with skip connections also known as generator. Afterwards, both the ground truth and generated images are fed to a convolution neural network (CNN) to extract their multi-level features. A dice loss function is…
Indian Institute of Science（印度科学学院）；University of Southern California（南加州大学）
Optic disc (OD) and optic cup (OC) are regions of prominent clinical interest in a retinal fundus image. They are the primary indicators of a glaucomatous condition. With the advent and success of deep learning for healthcare research, several approaches have been proposed for the segmentation of important features in retinal fundus images. We propose a novel approach for the simultaneous segmentation of the OD and OC using a residual encoder-decoder network (REDNet) based regional convolutional neural network (RCNN). The RED-RCNN is motivated by the Mask RCNN (MRCNN). Performance comparisons …
University of Engineering and Technology Taxila（塔克西拉工程技术大学）
Glaucoma is a severe eye disease that is known to deteriorate optic never fibers, causing cup size to increase, which could result in permanent loss of vision. Glaucoma is the second leading cause of blindness after cataract, but glaucoma being more dangerous as it is not curable. Early diagnoses and treatment of glaucoma can help to slow the progression of glaucoma and its damages. For the detection of glaucoma, the Cup to Disc ratio (CDR) provides significant information. The CDR depends heavily on the accurate segmentation of cup and disc regions. In this paper, we have proposed a modified …
Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
Diabetic Retinopathy (DR) is a leading cause of vision loss globally. Yet despite its prevalence, the majority of affected people lack access to the specialized ophthalmologists and equipment required for assessing their condition. This can lead to delays in the start of treatment, thereby lowering their chances for a successful outcome. Machine learning systems that automatically detect the disease in eye fundus images have been proposed as a means of facilitating access to DR severity estimates for patients in remote regions or even for complementing the human expert's diagnosis. In this pap…
Inception Institute of Artificial Intelligence（人工智能起源研究所）
Retinal fundus images are widely used for the clinical screening and diagnosis of eye diseases. However, fundus images captured by operators with various levels of experience have a large variation in quality. Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. However, due to the special optical beam of fundus imaging and structure of the retina, natural image enhancement methods cannot be utilized directly to address this. In this paper, we first analyze the ophthalmoscope imaging system and simulate a reliable degradation of major inf…
Renmin University of China（中国人民大学）；Visionary Intelligence Ltd.（远景智能有限公司）
Glaucoma is a severe blinding disease, for which automatic detection methods are urgently needed to alleviate the scarcity of ophthalmologists. Many works have proposed to employ deep learning methods that involve the segmentation of optic disc and cup for glaucoma detection, in which the segmentation process is often considered merely as an upstream sub-task. The relationship between fundus images and segmentation masks in terms of joint decision-making in glaucoma assessment is rarely explored. We propose a novel segmentation-based information extraction and amalgamation method for the task …
Medical University of Vienna（维也纳医科大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；University of Porto（波尔图大学）；KU Leuven（鲁汶大学）；Universitat Politècnica de València（瓦伦西亚理工大学）；University of Florida（佛罗里达大学）；The Chinese University of Hong Kong（香港中文大学）；Gachon University（嘉泉大学）；Samsung SDS AI Research Center（三星SDS人工智能研究中心）；Yale University（耶鲁大学）
Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy …
University of Bournemouth（伯恩茅斯大学）；École de Technolgie Superieure de Montréal（蒙特利尔高等技术学院）；Diagnos Inc.（Diagnos公司）；CRCHUM University of Montreal Hospital Centre（蒙特利尔大学医院研究中心）
Assessing the degree of disease severity in biomedical images is a task similar to standard classification but constrained by an underlying structure in the label space. Such a structure reflects the monotonic relationship between different disease grades. In this paper, we propose a straightforward approach to enforce this constraint for the task of predicting Diabetic Retinopathy (DR) severity from eye fundus images based on the well-known notion of Cost-Sensitive classification. We expand standard classification losses with an extra term that acts as a regularizer, imposing greater penaltie…
Automated Computer Aided diagnostic tools can be used for the early detection of glaucoma to prevent irreversible vision loss. In this work, we present a Multi-task Convolutional Neural Network (CNN) that jointly segments the Optic Disc (OD), Optic Cup (OC) and predicts the presence of glaucoma in color fundus images. The CNN utilizes a combination of image appearance features and structural features obtained from the OD-OC segmentation to obtain a robust prediction. The use of fewer network parameters and the sharing of the CNN features for multiple related tasks ensures the good generalizabi…
JIS College of Engineering（JIS工程学院）；Kalyani Govt. Engg. College（卡利亚尼政府工程学院）
Digital Retinal Fundus Images helps to detect various ophthalmic diseases by detecting morphological changes in optical cup, optical disc and macula. Present work proposes a method for the authentication of medical images based on Discrete Wavelet Transformation (DWT) and Spread Spectrum. Proper selection of the Non Region of Interest (NROI) for watermarking is crucial, as the area under concern has to be the least required portion conveying any medical information. Proposed method discusses both the selection of least impact area and the blind watermarking technique. Watermark is embedded wit…
Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade
Nikookari Eye Hospital（尼库卡里眼科医院）；Tabriz University of Medical Sciences（大不里士医科大学）；Cardiovascular Research Center（心血管研究中心）；Research Center for Evidence‑Based Medicine（循证医学研究中心）；Iranian EBM Centre: A Joanna Briggs Institute Affiliated Group（伊朗循证医学中心：乔安娜·布里格斯研究所附属机构）；Tabriz Islamic Art University（大不里士伊斯兰艺术大学）；Institute for Intelligent Systems Research and Innovation (IISRI)（智能系统研究与创新研究所（IISRI））；Deakin University（迪肯大学）
KU Leuven（鲁汶大学）；UZ Leuven（鲁汶大学医院）；University of Porto（波尔图大学）；Centro Hospitalar e Universitário São João（圣若昂大学医院中心）；Hasselt University（哈瑟尔特大学）；University of Antwerp（安特卫普大学）；Flemish Institute for Technological Research（弗拉芒技术研究院）
Although unprecedented sensitivity and specificity values are reported, recent glaucoma detection deep learning models lack in decision transparency. Here, we propose a methodology that advances explainable deep learning in the field of glaucoma detection and vertical cup-disc ratio (VCDR), an important risk factor. We trained and evaluated deep learning models using fundus images that underwent a certain cropping policy. We defined the crop radius as a percentage of image size, centered on the optic nerve head (ONH), with an equidistant spaced range from 10%-60% (ONH crop policy). The inverse…
Sun Yat-sen University（中山大学）；Baidu Inc.（百度公司）；Agency for Science, Technology and Research（新加坡科技研究局）；CONICET（阿根廷国家科学技术研究委员会）；UNICEN（阿根廷中央国立大学）；Medical University of Vienna（维也纳医科大学）；VUNO Inc.（VUNO公司）；Tencent（腾讯）
With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-dev…
University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）；Med-X Research Institute, Shanghai Jiao Tong University（上海交通大学Med-X研究院）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）
Automated segmentation of the optic cup and disk on retinal fundus images is fundamental for the automated detection / analysis of glaucoma. Traditional segmentation approaches depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to be adapt to the clinical environment. Recently, deep learning methods based on fully convolutional networks (FCNs) have been successful in resolving segmentation problems. However, the reliance on large annotated training data is problematic when dealing with medical images. If a sufficient amount of ann…
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Glaucoma is one of the most severe eye diseases, characterized by rapid progression and leading to irreversible blindness. It is often the case that diagnostics is carried out when one's sight has already significantly degraded due to the lack of noticeable symptoms at early stage of the disease. Regular glaucoma screenings of the population shall improve early-stage detection, however the desirable frequency of etymological checkups is often not feasible due to the excessive load imposed by manual diagnostics on limited number of specialists. Considering the basic methodology to detect glauco…
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases（眼科学国家重点实验室、中山眼科中心、中山大学、广东省眼科学重点实验室和视觉科学、广东省眼科临床研究中心）；Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Hainan International College, Minzu University of China（海南国际学院，中国民族大学）；School of Information Technology, Faculty of Business and Hospitality, Torrens University Australia（澳大利亚托伦斯大学信息科技学院，商业与酒店管理学院）；AIM for Health Lab, Faculty of IT, Monash University, Australia（健康AIM实验室，墨尔本大学IT学院，澳大利亚）；Department of Ophthalmology, Guangzhou First People’s Hospital, the Second Affiliated Hospital of South China University of Technology（广州第一人民医院，华南理工大学第二附属医院）；The Third Xiangya Hospital of Central South University（中南大学湘雅医学院第三医院）
Propagating Structural Guidance: Synthesizing Fluorescein Angiography from Fundus Images and Sparse OCT Scans
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education（教育部新一代人工智能技术及其跨学科应用重点实验室）；Tianyuan Honors School, Nanjing Medical University（南京医科大学天元荣誉学院）；Nanjing University of Science and Technology（南京理工大学）；Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院眼科）
Opening the black box of deep learning: Validating the statistical association between explainable artificial intelligence (XAI) and clinical domain knowledge in fundus image-based glaucoma diagnosis
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Singapore National Eye Centre（新加坡国家眼科中心）
PLADEMA Institute, UNICEN（国立中央布宜诺斯艾利斯省大学PLADEMA研究所）；Centro de Oftalmología Martínez（马丁内斯眼科中心）
Deep learning is currently the state-of-the-art for automated detection of referable diabetic retinopathy (DR) from color fundus photographs (CFP). While the general interest is put on improving results through methodological innovations, it is not clear how good these approaches perform compared to standard deep classification models trained with the appropriate settings. In this paper we propose to model a strong baseline for this task based on a simple and standard ResNet-18 architecture. To this end, we built on top of prior art by training the model with a standard preprocessing strategy …
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Glaucoma is a leading cause of irreversible blindness. Accurate segmentation of the optic disc (OD) and cup (OC) from fundus images is beneficial to glaucoma screening and diagnosis. Recently, convolutional neural networks demonstrate promising progress in joint OD and OC segmentation. However, affected by the domain shift among different datasets, deep networks are severely hindered in generalizing across different scanners and institutions. In this paper, we present a novel patchbased Output Space Adversarial Learning framework (pOSAL) to jointly and robustly segment the OD and OC from diffe…
Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences（中国科学院慈溪生物医学工程研究所）；Institute for Infocomm Research, Agency for Science, Technology and Research（新加坡科技研究局资讯通信研究院）；Shanghai University（上海大学）
Retinal fundus photographs have been used in the diagnosis of many ocular diseases such as glaucoma, pathological myopia, age-related macular degeneration and diabetic retinopathy. With the development of computer science, computer aided diagnosis has been developed to process and analyse the retinal images automatically. One of the challenges in the analysis is that the quality of the retinal image is often degraded. For example, a cataract in human lens will attenuate the retinal image, just as a cloudy camera lens which reduces the quality of a photograph. It often obscures the details in t…
Baidu Inc.（百度公司）；Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）
Glaucoma causes irreversible vision loss due to damage to the optic nerve, and there is no cure for glaucoma.OCT imaging modality is an essential technique for assessing glaucomatous damage since it aids in quantifying fundus structures. To promote the research of AI technology in the field of OCT-assisted diagnosis of glaucoma, we held a Glaucoma OCT Analysis and Layer Segmentation (GOALS) Challenge in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 to provide data and corresponding annotations for researchers studying …
The cup-to-disc ratio (CDR) is one of the most significant indicator for glaucoma diagnosis. Different from the use of costly fully supervised learning formulation with pixel-wise annotations in the literature, this study investigates the feasibility of accurate CDR measurement in fundus images using only tight bounding box supervision. For this purpose, we develop a two-task network named as CDRNet for accurate CDR measurement, one for weakly supervised image segmentation, and the other for bounding-box regression. The weakly supervised image segmentation task is implemented based on generali…
Deep learning-based segmentation methods have been widely employed for automatic glaucoma diagnosis and prognosis. In practice, fundus images obtained by different fundus cameras vary significantly in terms of illumination and intensity. Although recent unsupervised domain adaptation (UDA) methods enhance the models' generalization ability on the unlabeled target fundus datasets, they always require sufficient labeled data from the source domain, bringing auxiliary data acquisition and annotation costs. To further facilitate the data efficiency of the cross-domain segmentation methods on the f…
Technische Universität Kaiserslautern（凯泽斯劳滕工业大学）；German Research Center for Artificial Intelligence GmbH (DFKI)（德国人工智能研究中心）；Opthalmology Clinic（眼科诊所）；National University of Science and Technology (NUST)（国立科技大学）；National Center of Artificial Intelligence（国家人工智能中心）；Smart Data and Knowledge Services（智能数据与知识服务部）
Scarcity of large publicly available retinal fundus image datasets for automated glaucoma detection has been the bottleneck for successful application of artificial intelligence towards practical Computer-Aided Diagnosis (CAD). A few small datasets that are available for research community usually suffer from impractical image capturing conditions and stringent inclusion criteria. These shortcomings in already limited choice of existing datasets make it challenging to mature a CAD system so that it can perform in real-world environment. In this paper we present a large publicly available retin…
Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images
International Medical College（国际医学院）；Chongqing Medical University（重庆医科大学）；The First Affiliated Hospital of Chongqing Medical University（重庆医科大学第一附属医院）；Centre for Frontier AI Research (CFAR)（前沿人工智能研究中心）；Agency for Science Technology and Research (A*STAR)（新加坡科技研究局）
Liverpool John Moores University（利物浦约翰摩尔斯大学）；Lancaster University（兰卡斯特大学）；Aravind Eye Hospital（阿拉温德眼科医院）；Slovak University of Technology（斯洛伐克理工大学）；Ulster University（阿尔斯特大学）
Glaucoma is a leading cause of irreversible vision impairment globally and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention which can prevent further visual field loss. To detect glaucoma, examination of the optic nerve head via fundus imaging can be performed, at the centre of which is the assessment of the optic cup and disc boundaries. Fundus imaging is non-invasive and low-cost; however, the image examination relies on subjective, time-consuming, and costly expert assessments. A timely question to ask is can artificial intelligence mimic gla…
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Accurate segmentation of the optic disc (OD) and cup (OC)in fundus images from different datasets is critical for glaucoma disease screening. The cross-domain discrepancy (domain shift) hinders the generalization of deep neural networks to work on different domain datasets.In this work, we present an unsupervised domain adaptation framework,called Boundary and Entropy-driven Adversarial Learning (BEAL), to improve the OD and OC segmentation performance, especially on the ambiguous boundary regions. In particular, our proposed BEAL frame-work utilizes the adversarial learning to encourage the b…
University of Electronic Science and Technology of China（电子科技大学）；Shanghai AI Lab（上海人工智能实验室）；ShanghaiTech University（上海科技大学）；Shanghai Jiao Tong University（上海交通大学）
Efficiently utilizing discriminative features is crucial for convolutional neural networks to achieve remarkable performance in medical image segmentation and is also important for model generalization across multiple domains, where letting model recognize domain-specific and domain-invariant information among multi-site datasets is a reasonable strategy for domain generalization. Unfortunately, most of the recent disentangle networks are not directly adaptable to unseen-domain datasets because of the limitations of offered data distribution. To tackle this deficiency, we propose Contrastive D…
Baidu Research（百度研究院）；Mind Vogue Lab（Mind Vogue实验室）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）
Diffusion probabilistic model (DPM) recently becomes one of the hottest topic in computer vision. Its image generation application such as Imagen, Latent Diffusion Models and Stable Diffusion have shown impressive generation capabilities, which aroused extensive discussion in the community. Many recent studies also found it is useful in many other vision tasks, like image deblurring, super-resolution and anomaly detection. Inspired by the success of DPM, we propose the first DPM based model toward general medical image segmentation tasks, which we named MedSegDiff. In order to enhance the step…
One of the leading causes of blindness is glaucoma, which is challenging to detect since it remains asymptomatic until the symptoms are severe. Thus, diagnosis is usually possible until the markers are easy to identify, i.e., the damage has already occurred. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. However, due to the nature of the disease, researchers still debate which markers qualify as a consistent glaucoma metric. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and an…
HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Cambridge（剑桥大学）；The Hong Kong University of Science and Technology（香港科技大学）；The University of Hong Kong（香港大学）
Segmentation of optic disc (OD) and optic cup (OC) is critical in automated fundus image analysis system. Existing state-of-the-arts focus on designing deep neural networks with one or multiple dense prediction branches. Such kind of designs ignore connections among prediction branches and their learning capacity is limited. To build connections among prediction branches, this paper introduces gradient boosting framework to deep classification model and proposes a gradient boosting network called BoostNet. Specifically, deformable side-output unit and aggregation unit with deep supervisions ar…
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Shenzhen Eye Hospital affiliated to Jinan University（暨南大学附属深圳眼科医院）
Ophthalmologists have used fundus images to screen and diagnose eye diseases. However, different equipments and ophthalmologists pose large variations to the quality of fundus images. Low-quality (LQ) degraded fundus images easily lead to uncertainty in clinical screening and generally increase the risk of misdiagnosis. Thus, real fundus image restoration is worth studying. Unfortunately, real clinical benchmark has not been explored for this task so far. In this paper, we investigate the real clinical fundus image restoration problem. Firstly, We establish a clinical dataset, Real Fundus (RF)…
Observing retinal fundus images by an ophthalmologist is a major diagnosis approach for glaucoma. However, it is still difficult to distinguish the features of the lesion solely through manual observations, especially, in glaucoma early phase. In this paper, we present two deep learning-based automated algorithms for glaucoma detection and optic disc and cup segmentation. We utilize the attention mechanism to learn pixel-wise features for accurate prediction. In particular, we present two convolutional neural networks that can focus on learning various pixel-wise level features. In addition, w…
Institute for Infocomm Research, Agency for Science, Technology and Research（新加坡科技研究局资讯通信研究院）；Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences（中国科学院慈溪生物医学工程研究所）；State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所信息安全国家重点实验室）
Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio (CDR) plays an important role in the screening and diagnosis of glaucoma. Thus, the accurate and automatic segmentation of optic disc (OD) and optic cup (OC) from fundus images is a fundamental task. Most existing methods segment them separately, and rely on hand-crafted visual feature from fundus images. In this paper, we propose a deep learning architecture, named M-Net, which solves the OD and OC segmentation jointly in a one-stage multi-label system. The proposed M-Net mainly consists of multi-s…
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；Healthcare Technology Innovation Centre（医疗技术创新中心）；Indian Institute of Information Technology Design & Manufacturing Kancheepuram（金奈设计与制造印度理工学院）；Zasti（Zasti公司）
Recently, state-of-the-art results have been achieved in semantic segmentation using fully convolutional networks (FCNs). Most of these networks employ encoder-decoder style architecture similar to U-Net and are trained with images and the corresponding segmentation maps as a pixel-wise classification task. Such frameworks only exploit class information by using the ground truth segmentation maps. In this paper, we propose a multi-task learning framework with the main aim of exploiting structural and spatial information along with the class information. We modify the decoder part of the FCN to…
The Chinese University of Hong Kong（香港中文大学）；Stanford University（斯坦福大学）；Shenzhen University（深圳大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Deep convolutional neural networks have significantly boosted the performance of fundus image segmentation when test datasets have the same distribution as the training datasets. However, in clinical practice, medical images often exhibit variations in appearance for various reasons, e.g., different scanner vendors and image quality. These distribution discrepancies could lead the deep networks to over-fit on the training datasets and lack generalization ability on the unseen test datasets. To alleviate this issue, we present a novel Domain-oriented Feature Embedding (DoFE) framework to improv…
Computer Vision Institute, Shenzhen University（深圳大学计算机视觉研究所）；Tencent Jarvis Lab（腾讯贾维斯实验室）
Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models. Generative adversarial network (GAN), which synthesize plausible images, is one of the potential solutions to address the problem. However, the existing GAN-based approaches are prone to fail at preserving image-objects in image-to-image (I2I) translation, which reduces their practicality on domain adaptation tasks. In this paper, we propose a novel GAN (namely MI$^2$GAN) to maintain image-contents during cross-domain I2I tran…
Beijing Normal University（北京师范大学）；Hong Kong Baptist University（香港浸会大学）；Henan University（河南大学）
Convex Shapes (CS) are common priors for optic disc and cup segmentation in eye fundus images. It is important to design proper techniques to represent convex shapes. So far, it is still a problem to guarantee that the output objects from a Deep Neural Convolution Networks (DCNN) are convex shapes. In this work, we propose a technique which can be easily integrated into the commonly used DCNNs for image segmentation and guarantee that outputs are convex shapes. This method is flexible and it can handle multiple objects and allow some of the objects to be convex. Our method is based on the dual…
University of Electronic Science and Technology of China（电子科技大学）；Shanghai AI Lab（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；ShanghaiTech University（上海科技大学）；Sensetime Research（商汤科技研究院）；Sichuan University（四川大学）；Sichuan Cancer Hospital and Institute（四川省肿瘤医院（四川省肿瘤研究所））
Generalization to previously unseen images with potential domain shifts and different styles is essential for clinically applicable medical image segmentation, and the ability to disentangle domain-specific and domain-invariant features is key for achieving Domain Generalization (DG). However, existing DG methods can hardly achieve effective disentanglement to get high generalizability. To deal with this problem, we propose an efficient Contrastive Domain Disentanglement and Style Augmentation (CDDSA) framework for generalizable medical image segmentation. First, a disentangle network is propo…
Decoupling Wavelet Sub-bands for Single Source Domain Generalization in Fundus Image Segmentation
Indian Statistical Institute（印度统计研究所）；University of Oxford（牛津大学）；Radcliffe Department of Medicine, University of Oxford（牛津大学拉德克利夫医学系）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）
David R. Cheriton School of Computer Science（大卫·R·切里顿计算机科学学院）；University of Waterloo（滑铁卢大学）
Analysis of retinal fundus images is essential for eye-care physicians in the diagnosis, care and treatment of patients. Accurate fundus and/or retinal vessel maps give rise to longitudinal studies able to utilize multimedia image registration and disease/condition status measurements, as well as applications in surgery preparation and biometrics. The segmentation of retinal morphology has numerous applications in assessing ophthalmologic and cardiovascular disease pathologies. The early detection of many such conditions is often the most effective method for reducing patient risk. Computer ai…
Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究所）；University of Electronic Science and Technology of China（电子科技大学）
Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn image features that incorporate large context while keep high spatial resolutions. To approach this goal, the most widely used methods -- U-Net and variants, extract and fuse multi-scale features. However, the fused features still have small "effective receptive fields" with a focus on local image cues, limiting their performance. In this work, we propose Segtran, an alternative segmentation framework based on transform…
