We propose a new deep learning approach for automatic detection and segmentation of fluid within retinal OCT images. The proposed framework utilizes both ResNet and Encoder-Decoder neural network architectures. When training the network, we apply a novel data augmentation method called myopic warping together with standard rotation-based augmentation to increase the training set size to 45 times the original amount. Finally, the network output is post-processed with an energy minimization algorithm (graph cut) along with a few other knowledge guided morphological operations to finalize the segmentation process. Based on OCT imaging data and its ground truth from the RETOUCH challenge, the proposed system achieves dice indices of 0.522, 0.682, and 0.612, and average absolute volume differences of 0.285, 0.115, and 0.156 mm$^3$ for intaretinal fluid, subretinal fluid, and pigment epithelial detachment respectively.
Automated Erythrocyte Detection and Tracking for Retinal Blood Flow Quantification in Erythrocyte-Mediated Angiography
Department of Bioengineering, University of Maryland, College Park, MD 20742, USA（生物工程系，马里兰大学，学院公园，MD 20742，美国）；Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA（眼科学与视觉科学系，马里兰大学医学院，巴尔的摩，MD 21201，美国）；Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA（电气与计算机工程系，马里兰大学，学院公园，MD 20742，美国）
Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning. Our method not only performs end-to-end segmentation of retinal layers, but also gives the pixel wise uncertainty measure of the segmentation output. The generated uncertainty map can be used to identify erroneously segmented image regions which is useful in downstream analysis. We have validated our method on a dataset of 1487 images obtained from 15 …
University of Bern（伯尔尼大学）；RetinAI Medical GmbH（RetinAI Medical 公司）；Bern University Hospital（伯尔尼大学医院）
The automatic segmentation of retinal layer structures enables clinically-relevant quantification and monitoring of eye disorders over time in OCT imaging. Eyes with late-stage diseases are particularly challenging to segment, as their shape is highly warped due to pathological biomarkers. In this context, we propose a novel fully Convolutional Neural Network (CNN) architecture which combines dilated residual blocks in an asymmetric U-shape configuration, and can segment multiple layers of highly pathological eyes in one shot. We validate our approach on a dataset of late-stage AMD patients an…
Bruch's membrane (BM) segmentation on optical coherence tomography (OCT) is a pivotal step for the diagnosis and follow-up of age-related macular degeneration (AMD), one of the leading causes of blindness in the developed world. Automated BM segmentation methods exist, but they usually do not account for the anatomical coherence of the results, neither provide feedback on the confidence of the prediction. These factors limit the applicability of these systems in real-world scenarios. With this in mind, we propose an end-to-end deep learning method for automated BM segmentation in AMD patients.…
Medical University of Vienna（维也纳医科大学）；Medical University Vienna（维也纳医科大学）
Optical coherence tomography (OCT) has become the most important imaging modality in ophthalmology. A substantial amount of research has recently been devoted to the development of machine learning (ML) models for the identification and quantification of pathological features in OCT images. Among the several sources of variability the ML models have to deal with, a major factor is the acquisition device, which can limit the ML model's generalizability. In this paper, we propose to reduce the image variability across different OCT devices (Spectralis and Cirrus) by using CycleGAN, an unsupervis…
Centro de Investigación CITIC（CITIC研究中心）；Universidade da Coruña（拉科鲁尼亚大学）；Instituto de Investigación Biomédica de A Coruña (INIBIC)（拉科鲁尼亚生物医学研究所（INIBIC））
The study of the retinal vasculature is a fundamental stage in the screening and diagnosis of many diseases. A complete retinal vascular analysis requires to segment and classify the blood vessels of the retina into arteries and veins (A/V). Early automatic methods approached these segmentation and classification tasks in two sequential stages. However, currently, these tasks are approached as a joint semantic segmentation task, as the classification results highly depend on the effectiveness of the vessel segmentation. In that regard, we propose a novel approach for the simultaneous segmentat…
The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice. Supervised- or weakly supervised training enables the detection of findings that are known a priori. It does not scale well, and a priori definition limits the vocabulary of markers to known entities reducing the accuracy of diagnosis and prognosis. Here, we propose the identification of anomalies in large-scale medical imaging data using healthy examples as a reference. We detect and categorize candidates for anomaly findings untypical for th…
University of Electronic Science and Technology of China（电子科技大学）；McGill University（麦吉尔大学）；Mila - Quebec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）
Most of the existing deep learning based methods for vessel segmentation neglect two important aspects of retinal vessels, one is the orientation information of vessels, and the other is the contextual information of the whole fundus region. In this paper, we propose a robust Orientation and Context Entangled Network (denoted as OCE-Net), which has the capability of extracting complex orientation and context information of the blood vessels. To achieve complex orientation aware, a Dynamic Complex Orientation Aware Convolution (DCOA Conv) is proposed to extract complex vessels with multiple ori…
University of Nottingham（诺丁汉大学）；Shenzhen University（深圳大学）；Wuhan University of Technology（武汉理工大学）；University of Leicester（莱斯特大学）
In this paper, we propose a novel retinal layer boundary model for segmentation of optical coherence tomography (OCT) images. The retinal layer boundary model consists of 9 open parametric contours representing the 9 retinal layers in OCT images. An intensity-based Mumford-Shah (MS) variational functional is first defined to evolve the retinal layer boundary model to segment the 9 layers simultaneously. By making use of the normals of open parametric contours, we construct equal sized adjacent narrowbands that are divided by each contour. Regional information in each narrowband can thus be int…
University of Rostock（罗斯托克大学）；Rostock University Medical Center（罗斯托克大学医学中心）
Advances in optical coherence tomography (OCT) have enabled noninvasive imaging of substructures of the human retina with high spatial resolution. OCT examinations are now a standard procedure in clinics and an integral part of ophthalmic research. The interpretation of the OCT helps ophthalmologists understand the impact of various retinal and systemic diseases on the structure of the retina in a way not previously possible. In the early stages of retinal diseases, however, the identification and analysis of small and localized substructural changes in the retina remains a challenge. We prese…
Optical Coherence Tomography (OCT) is an emerging technique in the field of biomedical imaging, with applications in ophthalmology, dermatology, coronary imaging etc. Due to the underlying physics, OCT images usually suffer from a granular pattern, called speckle noise, which restricts the process of interpretation. Here, a sparse and low rank decomposition based method is used for speckle reduction in retinal OCT images. This technique works on input data that consists of several B-scans of the same location. The next step is the batch alignment of the images using a sparse and low-rank decom…
University of Science and Technology of China（中国科学技术大学）
Segmentation of multiple anatomical structures is of great importance in medical image analysis. In this study, we proposed a $\mathcal{W}$-net to simultaneously segment both the optic disc (OD) and the exudates in retinal images based on the multi-task learning (MTL) scheme. We introduced a class-balanced loss and a multi-task weighted loss to alleviate the imbalanced problem and to improve the robustness and generalization property of the $\mathcal{W}$-net. We demonstrated the effectiveness of our approach by applying five-fold cross-validation experiments on two public datasets e\_ophtha\_E…
Simultaneous column-based deep learning progression analysis of atrophy associated with AMD in longitudinal OCT studies
School of Computer Science and Engineering, The Hebrew University of Jerusalem（耶路撒冷希伯来大学计算机科学与工程学院）；The Alexander Grass Center for Bioengineering, The Hebrew University of Jerusalem（耶路撒冷希伯来大学亚历山大·格拉斯生物工程中心）；Hadassah Medical Center（哈达萨医疗中心）
From diagnosing neovascular diseases to detecting white matter lesions, accurate tiny vessel segmentation in fundus images is critical. Promising results for accurate vessel segmentation have been known. However, their effectiveness in segmenting tiny vessels is still limited. In this paper, we study retinal vessel segmentation by incorporating tiny vessel segmentation into our framework for the overall accurate vessel segmentation. To achieve this, we propose a new deep convolutional neural network (CNN) which divides vessel segmentation into two separate objectives. Specifically, we consider…
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations
Xiamen University（厦门大学）；National Institute for Data Science in Health and Medicine, Xiamen University（厦门大学健康医疗大数据国家研究院）；Tencent YouTu Lab（腾讯优图实验室）；Southern University of Science and Technology（南方科技大学）
University of Isfahan（伊斯法罕大学）；Duke University（杜克大学）；Duke University Medical Center（杜克大学医学中心）
Optical coherence tomography (OCT) helps ophthalmologists assess macular edema, accumulation of fluids, and lesions at microscopic resolution. Quantification of retinal fluids is necessary for OCT-guided treatment management, which relies on a precise image segmentation step. As manual analysis of retinal fluids is a time-consuming, subjective, and error-prone task, there is increasing demand for fast and robust automatic solutions. In this study, a new convolutional neural architecture named RetiFluidNet is proposed for multi-class retinal fluid segmentation. The model benefits from hierarchi…
With the introduction of spectral-domain optical coherence tomography (SDOCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, there is a critical need for the development of 3D segmentation methods for processing these data. We present here a novel 3D automatic segmentation method for retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume datasets are obtained by using a 3D smoothing filter and a 3D differential filter. Their linear combination is then calculated to generate new vo…
Simon Fraser University（西蒙弗雷泽大学）；University of British Columbia（不列颠哥伦比亚大学）；Medical University of Vienna（维也纳医科大学）；University of Zagreb（萨格勒布大学）
Purpose: Optical Coherence Tomography Angiography (OCT-A) permits visualization of the changes to the retinal circulation due to diabetic retinopathy (DR), a microvascular complication of diabetes. We demonstrate accurate segmentation of the vascular morphology for the superficial capillary plexus and deep vascular complex (SCP and DVC) using a convolutional neural network (CNN) for quantitative analysis. Methods: Retinal OCT-A with a 6x6mm field of view (FOV) were acquired using a Zeiss PlexElite. Multiple-volume acquisition and averaging enhanced the vessel network contrast used for training…
Tecnologico de Monterrey（蒙特雷理工学院）；Hospital Sant Joan de Déu（圣胡安德迪乌斯医院）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Research Centre for Biomedical Engineering (CREB)（生物医学工程研究中心（CREB））
The retinal vessel network studied through fundus images contributes to the diagnosis of multiple diseases not only found in the eye. The segmentation of this system may help the specialized task of analyzing these images by assisting in the quantification of morphological characteristics. Due to its relevance, several Deep Learning-based architectures have been tested for tackling this problem automatically. However, the impact of loss function selection on the segmentation of the intricate retinal blood vessel system hasn't been systematically evaluated. In this work, we present the comparis…
With the introduction of spectral-domain optical coherence tomography (OCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, the need for 3-D segmentation methods for processing such data is becoming increasingly important. We present a new 3D segmentation method for retinal OCT volume data, which generates an enhanced volume data by using pixel intensity, boundary position information, intensity changes on both sides of the border simultaneously, and preliminary discrete boundary points are found from all …
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation of training examples by experts. However, supervision does not scale well, due to the amount of necessary training examples, and the limitation of the marker vocabulary to known entities. In this proof-of-concept study, we propose unsupervised identification of anomalies as candidates for markers in retinal Optical Coherence Tomography (OCT) imaging…
Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所）；the Second Affiliated Hospital of Zhejiang University（浙江大学医学院附属第二医院）；Ningbo First Hospital（宁波市第一医院）；Edge Hill University（边山大学）；Ningbo University of Technology（宁波工程学院）
Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Voting-based Adaptive Feature Fusion multi-task network (VAFF-Net) for joint segmentation, detection, and classification of RV, FAZ, and RVJ in optical coherence tomography angiography (OCTA). A task-specific voting gate module is proposed to adaptively extract and fuse different features for specific tasks at two levels: featur…
Youtu Lab, Tencent（腾讯优图实验室）；School of Information Science and Engineering, Xiamen University（厦门大学信息科学与工程学院）
Retinal artery/vein (A/V) classification plays a critical role in the clinical biomarker study of how various systemic and cardiovascular diseases affect the retinal vessels. Conventional methods of automated A/V classification are generally complicated and heavily depend on the accurate vessel segmentation. In this paper, we propose a multi-task deep neural network with spatial activation mechanism that is able to segment full retinal vessel, artery and vein simultaneously, without the pre-requirement of vessel segmentation. The input module of the network integrates the domain knowledge of w…
Three-Dimensional Retinal Microvasculature Restoration in OCT Angiography
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学Casey眼科研究所）；Department of Biomedical Engineering, Oregon Health & Science University（俄勒冈健康与科学大学生物医学工程系）
Centro de Investigación CITIC（CITIC研究中心）；Universidade da Coruña（拉科鲁尼亚大学）；Instituto de Investigación Biomédica de A Coruña (INIBIC)（拉科鲁尼亚生物医学研究所（INIBIC））；Hospital Clínico San Carlos（圣卡洛斯临床医院）；Instituto de Investigación Sanitaria (IdISSC)（健康研究所（IdISSC））；Centro Internacional de Oftalmología Avanzada（国际高级眼科中心）
Age-related Macular Degeneration (AMD) is the predominant cause of blindness in developed countries, specially in elderly people. Moreover, its prevalence is increasing due to the global population ageing. In this scenario, early detection is crucial to avert later vision impairment. Nonetheless, implementing large-scale screening programmes is usually not viable, since the population at-risk is large and the analysis must be performed by expert clinicians. Also, the diagnosis of AMD is considered to be particularly difficult, as it is characterized by many different lesions that, in many case…
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic（义安理工学院电子与计算机工程系）；Department of Biomedical Engineering, School of Science and Technology, SIM University（新加坡管理学院大学科学与技术学院生物医学工程系）；Department of Biomedical Engineering, Faculty of Engineering, University of Malaya（马来亚大学工程学院生物医学工程系）；Department of Ophthalmology, Kasturba Medical College, Manipal（马尼帕尔卡斯图尔巴医学院眼科）；NIHR Moorfields Biomedical Research Centre（NIHR 莫菲尔德生物医学研究中心）
We have developed and trained a convolutional neural network to automatically and simultaneously segment optic disc, fovea and blood vessels. Fundus images were normalised before segmentation was performed to enforce consistency in background lighting and contrast. For every effective point in the fundus image, our algorithm extracted three channels of input from the neighbourhood of the point and forward the response across the 7 layer network. In average, our segmentation achieved an accuracy of 92.68 percent on the testing set from Drive database.
Indian Institute of Technology Bombay（印度理工学院孟买分校）；Indian Institute of Science（印度科学学院）；National Institute of Technology, Tiruchirappalli（印度国立理工学院蒂鲁吉拉伯利分校）
Diabetic Retinopathy is the leading cause of blindness in the world. At least 90\% of new cases can be reduced with proper treatment and monitoring of the eyes. However, scanning the entire population of patients is a difficult endeavor. Computer-aided diagnosis tools in retinal image analysis can make the process scalable and efficient. In this work, we focus on the problem of localizing the centers of the Optic disc and Fovea, a task crucial to the analysis of retinal scans. Current systems recognize the Optic disc and Fovea individually, without exploiting their relations during learning. W…
Research Group Ophthalmology, KU Leuven（KU Leuven眼科研究组）；Ophthalmology Department, UZ Leuven（UZ Leuven眼科部）；ESAT-PSI, KU Leuven（KU Leuven ESAT-PSI）；Hasselt University（哈塞尔特大学）；VITO NV
This investigation reports on the results of convolutional neural networks developed for the recently introduced PathologicAL Myopia (PALM) dataset, which consists of 1200 fundus images. We propose a new Optic Nerve Head (ONH)-based prediction enhancement for the segmentation of atrophy and fovea. Models trained with 400 available training images achieved an AUC of 0.9867 for pathological myopia classification, and a Euclidean distance of 58.27 pixels on the fovea localization task, evaluated on a test set of 400 images. Dice and F1 metrics for semantic segmentation of lesions scored 0.9303 an…
Robust Detection of Retinal Neovascularization in Widefield Optical Coherence Tomography
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学凯西眼科研究所）；Department of Biomedical Engineering, Oregon Health & Science University（俄勒冈健康与科学大学生物医学工程系）；Department of Ophthalmology, Mackay Memorial Hospital（马偕纪念医院眼科）；Department of Ophthalmology, Aichi Medical University（爱知医科大学眼科）
Automated surface segmentation of retinal layer is important and challenging in analyzing optical coherence tomography (OCT). Recently, many deep learning based methods have been developed for this task and yield remarkable performance. However, due to large spatial gap and potential mismatch between the B-scans of OCT data, all of them are based on 2D segmentation of individual B-scans, which may loss the continuity information across the B-scans. In addition, 3D surface of the retina layers can provide more diagnostic information, which is crucial in quantitative image analysis. In this stud…
Renmin University of China（中国人民大学）；Visionary Intelligence Ltd（远见智能有限公司）；Peking Union Medical College Hospital（北京协和医院）；Beijing Tongren Hospital（北京同仁医院）；Qingdao University（青岛大学）
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 pro…
OMSN and FAROS: OCTA Microstructure Segmentation Network and Fully Annotated Retinal OCTA Segmentation Dataset
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心眼科学国家重点实验室）；Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science（广东省眼科与视觉科学重点实验室）
National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）；Rochester Institute of Technology（罗切斯特理工学院）；TAJ Technologies Inc.（TAJ科技公司）
Segmentation of the left ventricle and quantification of various cardiac contractile functions is crucial for the timely diagnosis and treatment of cardiovascular diseases. Traditionally, the two tasks have been tackled independently. Here we propose a convolutional neural network based multi-task learning approach to perform both tasks simultaneously, such that, the network learns better representation of the data with improved generalization performance. Probabilistic formulation of the problem enables learning the task uncertainties during the training, which are used to automatically compu…
Xi'an University of Architecture and Technology（西安建筑科技大学）
Retinal vessels are important biomarkers for many ophthalmological and cardiovascular diseases. Hence, it is of great significance to develop automatic models for computer-aided diagnosis. Existing methods, such as U-Net follow the encoder-decoder pipeline, where detailed information is lost in the encoder in order to achieve a large field of view. Although spatial detailed information could be recovered partly in the decoder, while there is noise in the high-resolution feature maps of the encoder. And, we argue this encoder-decoder architecture is inefficient for vessel segmentation. In this …
This paper introduces a new approach to automatically quantify the severity of knee OA using X-ray images. Automatically quantifying knee OA severity involves two steps: first, automatically localizing the knee joints; next, classifying the localized knee joint images. We introduce a new approach to automatically detect the knee joints using a fully convolutional neural network (FCN). We train convolutional neural networks (CNN) from scratch to automatically quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross-entropy and mean-squared loss. This jo…
Simon Fraser University（西蒙菲莎大学）；University of British Columbia（不列颠哥伦比亚大学）；University of Western Australia（西澳大学）；Lions Eye Institute（狮子眼科研究所）；Vitreous Retina Macula Consultants of New York（纽约玻璃体视网膜黄斑 Consultants）；Manhattan Eye, Ear and Throat Hospital（曼哈顿眼耳鼻喉医院）；University of Zagreb（萨格勒布大学）
Automatic quantification of perifoveal vessel densities in optical coherence tomography angiography (OCT-A) images face challenges such as variable intra- and inter-image signal to noise ratios, projection artefacts from outer vasculature layers, and motion artefacts. This study demonstrates the utility of deep neural networks for automatic quantification of foveal avascular zone (FAZ) parameters and perifoveal vessel density of OCT-A images in healthy and diabetic eyes. OCT-A images of the foveal region were acquired using three OCT-A systems: a 1060nm Swept Source (SS)-OCT prototype, RTVue X…
Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Imperial College London（帝国理工学院）
We present a fully-automated segmentation and quantification of the left atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target late gadolinium-enhanced (LGE) image, and the other is an anatomical MRI from the same acquisition session. We formulate the joint distribution of images using a multivariate mixture model (MvMM), and employ the maximum likelihood estimator (MLE) for texture classification of the images simultaneously. The MvMM can also embed transformations assigned to the images to correct the misregistration. The iterated conditional mode algorithm is adopted …
University of Bern（伯尔尼大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University College London（伦敦大学学院）；Bern University Hospital（伯尔尼大学医院）
Detection of surgical instruments plays a key role in ensuring patient safety in minimally invasive surgery. In this paper, we present a novel method for 2D vision-based recognition and pose estimation of surgical instruments that generalizes to different surgical applications. At its core, we propose a novel scene model in order to simultaneously recognize multiple instruments as well as their parts. We use a Convolutional Neural Network architecture to embody our model and show that the cross-entropy loss is well suited to optimize its parameters which can be trained in an end-to-end fashion…
Previous approaches using deep learning algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using spectral-domain optical coherence tomography (SDOCT) data to train a deep learning algorithm to quantify glaucomatous structural damage on optic disc photographs. The dataset included 32,820 pairs of optic disc photos and SDOCT retinal nerve fiber layer (RNFL) scans from 2,312 eyes of 1,198 subjects. A deep learning convolutional neural network was trained to assess optic dis…
Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations
Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；School of Medicine and Health, TUM University Hospital（医学与健康学院，塔尔博恩大学医院）；School of Computation, Information and Technology, TUM（计算、信息与技术学院，塔尔博恩大学）；Weill Cornell Medicine, Cornell University, New York City（韦尔·科恩医学中心，康奈尔大学，纽约市）
Cardiac left ventricle (LV) quantification is among the most clinically important tasks for identification and diagnosis of cardiac diseases, yet still a challenge due to the high variability of cardiac structure and the complexity of temporal dynamics. Full quantification, i.e., to simultaneously quantify all LV indices including two areas (cavity and myocardium), six regional wall thicknesses (RWT), three LV dimensions, and one cardiac phase, is even more challenging since the uncertain relatedness intra and inter each type of indices may hinder the learning procedure from better convergence…
University of Cambridge（剑桥大学）；Chinese University of Hong Kong（香港中文大学）
We address the problem of automated nuclear segmentation, classification, and quantification from Haematoxylin and Eosin stained histology images, which is of great relevance for several downstream computational pathology applications. In this work, we present a solution framed as a simultaneous semantic and instance segmentation framework. Our solution is part of the Colon Nuclei Identification and Counting (CoNIC) Challenge. We first train a semantic and instance segmentation model separately. Our framework uses as backbone HoverNet and Cascade Mask-RCNN models. We then ensemble the results …
Glo-UMF: A Unified Multi-model Framework for Automated Morphometry of Glomerular Ultrastructural Characterization
School of Biomedical Engineering（生物医学工程学院）；Southern Medical University（南方医科大学）；Guangdong Provincial Key Laboratory of Medical Image Processing（广东省医学影像处理重点实验室）；Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology（广东省医学影像与诊断技术工程实验室）；Department of Pathology, School of Basic Medical Sciences（基础医学学院病理学系）；Guangzhou Huayin Medical Laboratory Center（广州华英医学实验室中心）；Central Laboratory（中心实验室）；Department of Nephrology（肾内科）
A Deep Learning-Based Unified Framework for Red Lesions Detection on Retinal Fundus Images
Computer and Information Science College, King Saud University（沙特王后大学计算机与信息科学学院）；Department of Ophthalmology, College of Medicine, Princess Nourah bint Abdulrahman University（普里丝特努尔·阿卜杜勒拉赫曼大学医学院眼科系）
SLOctolyzer: Fully automatic analysis toolkit for segmentation and feature extracting in scanning laser ophthalmoscopy images
University of Edinburgh（爱丁堡大学）；Institute for Regeneration and Repair, University of Edinburgh（爱丁堡大学再生与修复研究所）；Centre for Medical Informatics, University of Edinburgh（爱丁堡大学医学信息学中心）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Clinical Research Facility and Imaging, University of Edinburgh（爱丁堡大学临床研究设施与影像中心）；School of Mathematics, University of Edinburgh（爱丁堡大学数学学院）；Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh（爱丁堡大学信息学院自适应与神经计算研究所）；Centre for Clinical Brain Sciences, University of Edinburgh（爱丁堡大学临床脑科学中心）
We propose an end-to-end deep neural network (DNN) which can simultaneously segment the left atrial (LA) cavity and quantify LA scars. The framework incorporates the continuous spatial information of the target by introducing a spatially encoded (SE) loss based on the distance transform map. Compared to conventional binary label based loss, the proposed SE loss can reduce noisy patches in the resulting segmentation, which is commonly seen for deep learning-based methods. To fully utilize the inherent spatial relationship between LA and LA scars, we further propose a shape attention (SA) mechan…
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 …
