Automated eye disease classification method from anterior eye image using anatomical structure focused image classification technique
Nagoya University（名古屋大学）；Tokyo Dental College（东京牙科大学）；Kyoto Prefectural University of Medicine（京都府立医科大学）；University of Tsukuba（筑波大学）；National Institute of Informatics（国立信息学研究所）
Masahiro Oda, Takefumi Yamaguchi, Hideki Fukuoka, Yuta Ueno, Kensaku Mori
备注：
Accepted paper as a poster presentation at SPIE Medical Imaging 2020, Houston, TX, USA
发表信息：
Proceedings of SPIE Medical Imaging 2020: Computer-Aided Diagnosis, Vol.11314, 1131446
This paper presents an automated classification method of infective and non-infective diseases from anterior eye images. Treatments for cases of infective and non-infective diseases are different. Distinguishing them from anterior eye images is important to decide a treatment plan. Ophthalmologists distinguish them empirically. Quantitative classification of them based on computer assistance is necessary. We propose an automated classification method of anterior eye images into cases of infective or non-infective disease. Anterior eye images have large variations of the eye position and brightness of illumination. This makes the classification difficult. If we focus on the cornea, positions of opacified areas in the corneas are different between cases of the infective and non-infective diseases. Therefore, we solve the anterior eye image classification task by using an object detection approach targeting the cornea. This approach can be said as "anatomical structure focused image classification". We use the YOLOv3 object detection method to detect corneas of infective disease and corneas of non-infective disease. The detection result is used to define a classification result of a image. In our experiments using anterior eye images, 88.3% of images were correctly classified by the proposed method.
National University of Singapore（新加坡国立大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡全国眼科中心）；Cardiff University（卡迪夫大学）；Newcastle Research and Innovation Institute（纽卡斯尔研究创新研究所）；National University Hospital（国立大学医院）
Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle closure glaucoma. In this study, we developed a deep convolutional neural network (DCNN) for the localization of the scleral spur, and the segmentation of anterior segment structures (iris, corneo-sclera shell, anterior chamber). With limited training data, the DCNN was able to detect the scleral spur on unseen ASOCT images as accurately as an experienced ophthal…
Research and Academic Computer Network (NASK)（研究与学术计算机网络（NASK））；Warsaw University of Technology（华沙理工大学）；Medical University of Warsaw（华沙医科大学）
This paper presents a database of iris images collected from disease affected eyes and an analysis related to the influence of ocular diseases on iris recognition reliability. For that purpose we have collected a database of iris images acquired for 91 different eyes during routine ophthalmology visits. This collection gathers samples for healthy eyes as well as those with various eye pathologies, including cataract, acute glaucoma, posterior and anterior synechiae, retinal detachment, rubeosis iridis, corneal vascularization, corneal grafting, iris damage and atrophy and corneal ulcers, haze …
Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）
Optical Coherence Tomography (OCT) is an imaging modality that has been widely adopted for visualizing corneal, retinal and limbal tissue structure with micron resolution. It can be used to diagnose pathological conditions of the eye, and for developing pre-operative surgical plans. In contrast to the posterior retina, imaging the anterior tissue structures, such as the limbus and cornea, results in B-scans that exhibit increased speckle noise patterns and imaging artifacts. These artifacts, such as shadowing and specularity, pose a challenge during the analysis of the acquired volumes as they…
We propose a fully automatic method to find standardized view planes in 3D image acquisitions. Standard view images are important in clinical practice as they provide a means to perform biometric measurements from similar anatomical regions. These views are often constrained to the native orientation of a 3D image acquisition. Navigating through target anatomy to find the required view plane is tedious and operator-dependent. For this task, we employ a multi-scale reinforcement learning (RL) agent framework and extensively evaluate several Deep Q-Network (DQN) based strategies. RL enables a na…
INRIA Sophia Antipolis - Méditerranée（法国国家信息与自动化研究所索菲亚·安提波利斯-地中海研究中心）；Birla Institute of Technology and Science, Pilani（皮拉尼·博拉理工学院）；Université Côte d’Azur（蔚蓝海岸大学）
3D gaze estimation is about predicting the line of sight of a person in 3D space. Person-independent models for the same lack precision due to anatomical differences of subjects, whereas person-specific calibrated techniques add strict constraints on scalability. To overcome these issues, we propose a novel technique, Facial Landmark Heatmap Activated Multimodal Gaze Estimation (FLAME), as a way of combining eye anatomical information using eye landmark heatmaps to obtain precise gaze estimation without any person-specific calibration. Our evaluation demonstrates a competitive performance of a…
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
Diabetic retinopathy (DR) is the most common form of diabetic eye disease. Retinopathy can affect all diabetic patients and becomes particularly dangerous, increasing the risk of blindness, if it is left untreated. The success rate of its curability solemnly depends on diagnosis at an early stage. The development of automated computer aided disease diagnosis tools could help in faster detection of symptoms with a wider reach and reasonable cost. This paper proposes a method for the automated segmentation of retinal lesions and optic disk in fundus images using a deep fully convolutional neural…
Two-View Topogram-Based Anatomy-Guided CT Reconstruction for Prospective Risk Minimization
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡大学模式识别实验室）；Division of X-Ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ)（德国癌症研究中心X射线成像与计算机断层扫描部门）；Department of Physics and Astronomy, Ruprecht-Karls-University Heidelberg（海德堡大学物理与天文学系）；Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡大学埃尔朗根大学医院放射肿瘤科）；Department of Radiology and Nuclear Medicine, Klinikum Nürnberg, Paracelsus Medical University（帕拉塞尔苏斯医科大学纽伦堡医院放射与核医学科）
LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models
Yale University（耶鲁大学）；Imperial College London（伦敦帝国学院）；Australian National University（澳大利亚国立大学）；University of Georgia（佐治亚大学）；National University of Singapore（新加坡国立大学）；RMIT University（皇家墨尔本理工大学）
Image Sciences Institute（影像科学研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）；Department of Radiology（放射科）
Fast and accurate anatomical landmark detection can benefit many medical image analysis methods. Here, we propose a method to automatically detect anatomical landmarks in medical images. Automatic landmark detection is performed with a patch-based fully convolutional neural network (FCNN) that combines regression and classification. For any given image patch, regression is used to predict the 3D displacement vector from the image patch to the landmark. Simultaneously, classification is used to identify patches that contain the landmark. Under the assumption that patches close to a landmark can…
Riemannian Shape Analysis of the Corpus Callosum in Kendall Space: Aging and Alzheimer's Disease
African Institute for Mathematical Sciences (AIMS), Rwanda（非洲数学科学研究所（卢旺达））；African Institute for Mathematical Sciences (AIMS), Senegal（非洲数学科学研究所（塞内加尔））
Enhanced Denoising of Optical Coherence Tomography Images Using Residual U-Net
IIT Bhubaneswar（印度理工学院布巴内斯瓦尔分校）；Aravind Eye Care Systems（阿拉文德眼科医疗系统）；Singapore Eye Research Institute（新加坡眼科研究所）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）
本研究提出基于 Residual U-Net 架构的增强去噪模型，有效降低 OCT 图像中的散斑噪声并保留组织细节，在 ASOCT 和 PSOCT 两种模态上均显著提升了图像质量指标，助力更精确的临床诊断。
