1) Objectives: This technical report presents a synthetic summary and the principal outcomes of the project R-Vessel-X ("Robust vascular network extraction and understanding within hepatic biomedical images") funded by the French Agence Nationale de la Recherche, and developed between 2019 and 2023. 2) Material and methods: We used datasets and tools publicly available such as IRCAD, Bullitt or VascuSynth toobtain real or synthetic angiographic images. The main contributions lie in the field of 3D angiographic image analysis: filtering, segmentation, modeling and simulation, with a specific focus on the liver. 3) Results: We paid a particular attention to open-source software diffusion of the developed methods, by means of 3D Slicer plugins for the liver anatomy segmentation (SlicerRVXLiverSegmentation) and vesselness filtering (Slicer-RVXVesselnessFilters), and an online demo for the generation of synthetic and realistic vessels in 2D and 3D (OpenCCO). 4) Conclusion: The R-Vessel-X project provided extensive research outcomes, covering various topics related to 3D angiographic image analysis, such as filtering, segmentation, modeling and simulation. We also developed open-source and free softwares so that the research communities in biomedical engineering can use these results in their future research.
Tsinghua University（清华大学）；Beijing Anzhen Hospital, Capital Medical University（首都医科大学附属北京安贞医院）；The First Affiliated Hospital of Zhengzhou University（郑州大学第一附属医院）
Coronary angiography is an indispensable assistive technique for cardiac interventional surgery. Segmentation and extraction of blood vessels from coronary angiography videos are very essential prerequisites for physicians to locate, assess and diagnose the plaques and stenosis in blood vessels. This article proposes a new video segmentation framework that can extract the clearest and most comprehensive coronary angiography images from a video sequence, thereby helping physicians to better observe the condition of blood vessels. This framework combines a 3D convolutional layer to extract spati…
A Deep Learning Model for Coronary Artery Segmentation and Quantitative Stenosis Detection in Angiographic Images
School of Mathematical Sciences, Ocean University of China（中国海洋大学数学科学学院）；School of Haide, Ocean University of China（中国海洋大学海德学院）；School of Ocean and Atmosphere, Ocean University of China（中国海洋大学海洋与大气学院）；Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University（山东大学齐鲁医院（青岛））
Local-sensitive connectivity filter (ls-cf): A post-processing unsupervised improvement of the frangi, hessian and vesselness filters for multimodal vessel segmentation
Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR)（学术信息系（DAINF），技术联邦大学帕托布拉诺分校（UTFPR））；Graduate Program of Sciences Applied to Health Products, Universidade Federal Fluminense (UFF)（健康产品应用科学研究生项目，联邦大学弗洛里塞分校（UFF））；Institute of Technological Sciences (ICT), Universidade Federal de Itajuba (UNIFEI)（技术科学研究所，联邦大学伊塔比亚分校（UNIFEI））；Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology（电气工程与计算机科学系，卡利法科学技术大学）
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学凯西眼科研究所）；School of Physics and Optoelectronic Engineering, Foshan University（佛山大学物理与光电工程学院）；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）
Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net) to segment volumetric retinal fluid on optical coherence tomography (OCT) volume. Methods: 3 x 3-mm OCT scans were acquired on one eye by a 70-kHz OCT commercial AngioVue system (RTVue-XR; Optovue, Inc.) from 51 participants in a clinical diabetic retinopathy (DR) study (45 with retinal edema and 6 healthy controls). A CNN with U-Net-like architecture was constructed to detect and segment the retinal fluid. Cross-sectional OCT and angiography (OCTA) scans were used for training an…
The morphology of retinal blood vessels can indicate various diseases in the human body, and researchers have been working on automatic scanning and segmentation of retinal images to aid diagnosis. This project compares the performance of four neural network architectures in segmenting retinal images, using a combined dataset from different databases, namely the UNet, DR-VNet, UNet-ResNet and UNet-VGG.
SPOCKMIP: Segmentation of Vessels in MRAs with Enhanced Continuity using Maximum Intensity Projection as Loss
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；German Centre for Neurodegenerative Disease（德国神经退行性疾病中心）；Centre for Behavioural Brain Sciences（行为脑科学中心）；Human Technopole（人类技术园）；Genomics Research Centre（基因组学研究中心）
Vessel-Promoted OCT to OCTA Image Translation by Heuristic Contextual Constraints
The Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳港协同创新研究院）；Weizhi Meditech (Foshan) Co., Ltd（佛山伟智医疗科技有限公司）；Guangdong Weiren Meditech Co., Ltd（广东伟仁医疗科技有限公司）；South China University of Technology（华南理工大学）；Pazhou Lab（琶洲实验室）；Shenzhen Eye Institute（深圳眼科研究院）；Shenzhen Eye Hospital（深圳眼科医院）；Jinan University（暨南大学）；Shaanxi Eye Hospital（陕西省眼科医院）；Xi’an People’s Hospital (Xi’an Fourth Hospital)（西安市人民医院（西安市第四医院））；Affiliated People’s Hospital of Northwest University（西北大学附属人民医院）
Queen Mary University of London（伦敦玛丽女王大学）；Barts Health NHS Trust（巴特国民保健信托基金会）
X-ray coronary angiography (XCA) is used to assess coronary artery disease and provides valuable information on lesion morphology and severity. However, XCA images are 2D and therefore limit visualisation of the vessel. 3D reconstruction of coronary vessels is possible using multiple views, however lumen border detection in current software is performed manually resulting in limited reproducibility and slow processing time. In this study we propose 3DAngioNet, a novel deep learning (DL) system that enables rapid 3D vessel mesh reconstruction using 2D XCA images from two views. Our approach lea…
3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning
School of Biomedical Engineering \& State Key Laboratory of Advanced Medical Materials；Devices, ShanghaiTech University, Shanghai, China；National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, China；School of Electronic Information；Communications, Huazhong University of Science；Department of Computer Science \& Engineering, Texas A\&M University, USA
The motivation of our work is to present a new visualization-guided computing paradigm to combine direct 3D volume processing and volume rendered clues for effective 3D exploration such as extracting and visualizing microstructures in-vivo. However, it is still challenging to extract and visualize high fidelity 3D vessel structure due to its high sparseness, noisiness, and complex topology variations. In this paper, we present an end-to-end deep learning method, VC-Net, for robust extraction of 3D microvasculature through embedding the image composition, generated by maximum intensity projecti…
Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Henan University of Chinese Medicine（河南中医药大学）
3D to 2D retinal vessel segmentation is a challenging problem in Optical Coherence Tomography Angiography (OCTA) images. Accurate retinal vessel segmentation is important for the diagnosis and prevention of ophthalmic diseases. However, making full use of the 3D data of OCTA volumes is a vital factor for obtaining satisfactory segmentation results. In this paper, we propose a Progressive Attention-Enhanced Network (PAENet) based on attention mechanisms to extract rich feature representation. Specifically, the framework consists of two main parts, the three-dimensional feature learning path and…
Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation
LaTIM UMR 1101, Inserm（法国国家健康与医学研究院LaTIM UMR 1101研究所）；University of Western Brittany（西布列塔尼大学）；University Hospital of Brest（布雷斯特大学医院）；University Hospital of Poitiers（普瓦捷大学医院）；ICube UMR 7357, CNRS（法国国家科学研究中心ICube UMR 7357研究所）；IMT Atlantique（大西洋电信学院）
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…
In medical imaging, there are clinically relevant segmentation tasks where the output mask is a projection to a subset of input image dimensions. In this work, we propose a novel convolutional neural network architecture that can effectively learn to produce a lower-dimensional segmentation mask than the input image. The network restores encoded representation only in a subset of input spatial dimensions and keeps the representation unchanged in the others. The newly proposed projective skip-connections allow linking the encoder and decoder in a UNet-like structure. We evaluated the proposed m…
Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging
University College London（伦敦大学学院）；Centre for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；Indiana University（印第安纳大学）；European Synchrotron Radiation Facility（欧洲同步辐射装置）；Laboratoire d’Anatomie Des Alpes Françaises（法国阿尔卑斯解剖实验室）；Institute of Pathology, Hannover Medical School（汉诺威医学院病理研究所）；Institute of Functional and Clinical Anatomy, University Medical Center of the Johannes Gutenberg-University Mainz（美因茨大学约翰内斯·古滕贝格大学医学中心功能与临床解剖研究所）；German Center for Lung Research (DZL)（德国肺研究中心）；Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH)（汉诺威终末期及阻塞性肺病生物医学研究中心）
Topologically Faithful Multi-class Segmentation in Medical Images
School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学伊萨尔河畔 Klinikum 医学院与健康学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Munich Data Science Institute, Technical University of Munich（慕尼黑工业大学慕尼黑数据科学研究所）；Department of Quantitative Biomedicine, University of Zurich（苏黎世大学定量生物医学系）
