Automatic blood vessel extraction from 3D medical images is crucial for vascular disease diagnoses. Existing methods based on convolutional neural networks (CNNs) may suffer from discontinuities of extracted vessels when segmenting such thin tubular structures from 3D images. We argue that preserving the continuity of extracted vessels requires to take into account the global geometry. However, 3D convolutions are computationally inefficient, which prohibits the 3D CNNs from sufficiently large receptive fields to capture the global cues in the entire image. In this work, we propose a hybrid representation learning approach to address this challenge. The main idea is to use CNNs to learn local appearances of vessels in image crops while using another point-cloud network to learn the global geometry of vessels in the entire image. In inference, the proposed approach extracts local segments of vessels using CNNs, classifies each segment based on global geometry using the point-cloud network, and finally connects all the segments that belong to the same vessel using the shortest-path algorithm. This combination results in an efficient, fully-automatic and template-free approach to centerline extraction from 3D images. We validate the proposed approach on CTA datasets and demonstrate its superior performance compared to both traditional and CNN-based baselines.
An automated framework for brain vessel centerline extraction from CTA images
Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所）；Department of Radiology, Erasmus MC（伊拉斯姆斯大学医学中心放射科）；Department of Radiology, Maastricht UMC（马斯特里赫特大学医学中心放射科）；Faculty of Applied Sciences, Delft University of Technology（代尔夫特理工大学应用科学学院）
Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction
Université de Reims Champagne Ardenne（兰斯香槟阿登大学）；Univ Lyon（里昂大学）；INSA-Lyon（里昂国立应用科学学院）；Université Claude Bernard Lyon 1（里昂第一大学）；UJM-Saint Etienne（圣埃蒂安大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；CREATIS（CREATIS研究院）
China University of Petroleum（中国石油大学）；Qingdao Municipal Hospital（青岛市市立医院）；University of Chinese Academy of Sciences（中国科学院大学）
Liver cancer is one of the most common malignant diseases in the world. Segmentation and labeling of liver tumors and blood vessels in CT images can provide convenience for doctors in liver tumor diagnosis and surgical intervention. In the past decades, many state-of-the-art medical image segmentation algorithms appeared during this period. With the development of embedded devices, embedded deployment for medical segmentation and automatic reconstruction brings prospects for future automated surgical tasks. Yet, most of the existing segmentation methods mostly care about the spatial feature co…
Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance
Wuhan University of Technology（武汉理工大学）；National Taiwan University of Science and Technology（台湾科技大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
A topology-preserving three-stage framework for fully-connected coronary artery extraction
Center for Digital Media Computing, School of Film, School of Informatics, Xiamen University（厦门大学电影学院、信息学院数字媒体计算中心）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）；Peking Union Medical College Hospital（北京协和医院）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；City University of Hong Kong（香港城市大学）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segmentation. Extensive image-processing techniques have been developed to overcome the problems of vessel tracking that are mainly attributed to the complex morphologies of vessels and image characteristics of angiography. This paper presents a literature review on vessel-tracking methods, focusing on machine-learning-based methods. First, the convent…
Vessel tracing by modeling vascular structures in 3D medical images with centerlines and radii can provide useful information for vascular health. Existing algorithms have been developed but there are certain persistent problems such as incomplete or inaccurate vessel tracing, especially in complicated vascular beds like the intracranial arteries. We propose here a deep learning based open curve active contour model (DOST) to trace vessels in 3D images. Initial curves were proposed from a centerline segmentation neural network. Then data-driven machine knowledge was used to predict the stretch…
Quantitative analysis of vessel wall structures by automated vessel wall segmentation provides useful imaging biomarkers in evaluating atherosclerotic lesions and plaque progression time-efficiently. To quantify vessel wall features, drawing lumen and outer wall contours of the artery of interest is required. To alleviate manual labor in contour drawing, some computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and boundary initialization are needed. In addition, the prior knowledge of the ring shape of vessel wall is not taken into considera…
With the injection of contrast material into blood vessels, multi-phase contrasted CT images can enhance the visibility of vessel networks in the human body. Reconstructing the 3D geometric morphology of liver vessels from the contrasted CT images can enable multiple liver preoperative surgical planning applications. Automatic reconstruction of liver vessel morphology remains a challenging problem due to the morphological complexity of liver vessels and the inconsistent vessel intensities among different multi-phase contrasted CT images. On the other side, high integrity is required for the 3D…
Junctions in the retinal vasculature are key points to be able to extract its topology, but they vary in appearance, depending on vessel density, width and branching/crossing angles. The complexity of junction patterns is usually accompanied by a scarcity of labels, which discourages the usage of very deep networks for their detection. We propose a multi-task network, generating labels for vessel interior, centerline, edges and junction patterns, to provide additional information to facilitate junction detection. After the initial detection of potential junctions in junction-selective probabil…
Technical University of Munich（慕尼黑工业大学）；Eindhoven University of Technology（埃因霍温理工大学）
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic; particularly preserving connectedness: in the case of vascular networks, missing a connected vessel entirely alters the blood-flow dynamics. We introduce a novel similarity measure termed centerlineDice (short clDice), which is calculated on the intersection of the segmentation masks and their (morphological) skeleta. We theoretically prove that clDice guarantees topology preservation up …
Generating computational anatomical models of cerebrovascular networks is vital for improving clinical practice and understanding brain oxygen transport. This is achieved by extracting graph-based representations based on pre-mapping of vascular structures. Recent graphing methods can provide smooth vessels trajectories and well-connected vascular topology. However, they require water-tight surface meshes as inputs. Furthermore, adding vessels radii information on their graph compartments restricts their alignment along vascular centerlines. Here, we propose a novel graphing scheme that works …
Erasmus University Medical Center（伊拉斯姆斯大学医学中心）；Ecole des Mines de Saint-Etienne（圣艾蒂安国立矿业学院）；Ecole Centrale Marseille（马赛中央理工学院）；TU Delft（代尔夫特理工大学）
Scoliosis is a condition defined by an abnormal spinal curvature. For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles. We propose an automated method for the estimation of Cobb angles from X-ray scans. First, the centerline of the spine was segmented using a cascade of two convolutional neural networks. After smoothing the centerline, Cobb angles were automatically estimated using the derivative of the centerline. We evaluated the results using the mean absolute error and the average symmetric mean absolute percentage error between the manual …
RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction
Huadong Hospital, affiliated to Fudan University（复旦大学附属华东医院）；Huashan Hospital, affiliated to Fudan University（复旦大学附属华山医院）；Academy of Forensic Science（司法鉴定科学研究院）；Carnegie Mellon University（卡内基梅隆大学）；Harvard University（哈佛大学）；Boston College（波士顿学院）；University of California San Diego（加州大学圣地亚哥分校）
Philips Research North America（飞利浦北美研究院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Accurate, real-time segmentation of vessel structures in ultrasound image sequences can aid in the measurement of lumen diameters and assessment of vascular diseases. This, however, remains a challenging task, particularly for extremely small vessels that are difficult to visualize. We propose to leverage the rich spatiotemporal context available in ultrasound to improve segmentation of small-scale lower-extremity arterial vasculature. We describe efficient deep learning methods that incorporate temporal, spatial, and feature-aware contextual embeddings at multiple resolution scales while join…
Eindhoven University of Technology（埃因霍温理工大学）；University Medical Center Utrecht（乌得勒支大学医学中心）
Deep learning approaches may help radiologists in the early diagnosis and timely treatment of cerebrovascular diseases. Accurate cerebral vessel segmentation of Time-of-Flight Magnetic Resonance Angiographs (TOF-MRAs) is an essential step in this process. This study investigates deep learning approaches for automatic, fast and accurate cerebrovascular segmentation for TOF-MRAs. The performance of several data augmentation and selection methods for training a 2D and 3D U-Net for vessel segmentation was investigated in five experiments: a) without augmentation, b) Gaussian blur, c) rotation and …
Recently, many methods based on hand-designed convolutional neural networks (CNNs) have achieved promising results in automatic retinal vessel segmentation. However, these CNNs remain constrained in capturing retinal vessels in complex fundus images. To improve their segmentation performance, these CNNs tend to have many parameters, which may lead to overfitting and high computational complexity. Moreover, the manual design of competitive CNNs is time-consuming and requires extensive empirical knowledge. Herein, a novel automated design method, called Genetic U-Net, is proposed to generate a U…
Computer Science Department, Henry Samueli School of Engineering Stein Eye Institute, David Geffen School of Medicine University of California, Los Angeles, CA 90095, USA
The reliable segmentation of retinal vasculature can provide the means to diagnose and monitor the progression of a variety of diseases affecting the blood vessel network, including diabetes and hypertension. We leverage the power of convolutional neural networks to devise a reliable and fully automated method that can accurately detect, segment, and analyze retinal vessels. In particular, we propose a novel, fully convolutional deep neural network with an encoder-decoder architecture that employs dilated spatial pyramid pooling with multiple dilation rates to recover the lost content in the e…
AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo
Graz University of Technology（格拉茨工业大学）；Computer Algorithms for Medicine Laboratory（医学计算机算法实验室）；University Medicine Essen (AöR)（埃森大学医院（AöR））；Affiliated Dongyang Hospital of Wenzhou Medical University（温州医科大学附属东阳医院）
High Accuracy Pulmonary Vessel Segmentation for Contrast and Non-contrast CT Images and Clinical Evaluation
Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（北京协和医院，中国医学科学院 北京协和医学院）；Canon Medical Systems (China)（佳能医疗系统（中国））
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…
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.
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）；University of Liverpool（利物浦大学）；University of Southern California（南加州大学）；Southern University of Science and Technology（南方科技大学）；University of Chinese Academy of Sciences（中国科学院大学）
Optical Coherence Tomography Angiography (OCTA) has been increasingly used in the management of eye and systemic diseases in recent years. Manual or automatic analysis of blood vessel in 2D OCTA images (en face angiograms) is commonly used in clinical practice, however it may lose rich 3D spatial distribution information of blood vessels or capillaries that are useful for clinical decision-making. In this paper, we introduce a novel 3D vessel reconstruction framework based on the estimation of vessel depth maps from OCTA images. First, we design a network with structural constraints to predict…
DVasMesh: Deep Structured Mesh Reconstruction from Vascular Images for Dynamics Modeling of Vessels
Hong Kong Centre for Cerebro-cardiovascular Health Engineering（香港心脑血管健康工程中心）；School of Data Science, City University of Hong Kong（香港城市大学数据科学学院）；State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University（上海科技大学先进医用材料与器件国家重点实验室）；School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science（上海工程技术大学机械与汽车工程学院）；JinFeng Laboratory（金凤实验室）；Department of Biomedical Engineering, City University of Hong Kong（香港城市大学生物医学工程系）
Deep vessel segmentation with joint multi-prior encoding
LaTIM UMR 1101；Inserm（法国国家健康与医学研究院）；University of Western Brittany（西布列塔尼大学）；University Hospital of Brest（布雷斯特大学医院）；CONICET（阿根廷国家科学技术研究委员会）；ICube UMR 7357；CNRS（法国国家科学研究中心）；IMT Atlantique（大西洋电信学院）
In recent years, several automatic segmentation methods have been proposed for blood vessels in retinal fundus images, ranging from using cheap and fast trainable filters to complicated neural networks and even deep learning. One example of a filted-based segmentation method is B-COSFIRE. In this approach the image filter is trained with example prototype patterns, to which the filter becomes selective by finding points in a Difference of Gaussian response on circles around the center with large intensity variation. In this paper we discuss and evaluate several of these vessel segmentation met…
Vessel segmentation in fundus is a key diagnostic capability in ophthalmology, and there are various challenges remained in this essential task. Early approaches indicate that it is often difficult to obtain desirable segmentation performance on thin vessels and boundary areas due to the imbalance of vessel pixels with different thickness levels. In this paper, we propose a novel two-stream Meticulous-Processing Network (MP-Net) for tackling this problem. To pay more attention to the thin vessels and boundary areas, we firstly propose an efficient hierarchical model automatically stratifies th…
Automatic segmentation of retina vessels plays a pivotal role in clinical diagnosis of prevalent eye diseases, such as, Diabetic Retinopathy or Age-related Macular Degeneration. Due to the complex construction of blood vessels, with drastically varying thicknesses, accurate vessel segmentation can be quite a challenging task. In this work we show that it is possible to achieve near state-of-the-art performance, by crafting a careful thought pre-processing pipeline, without having to resort to complex networks and/or training routines. We also show that our model is able to maintain the same hi…
Beijing University of Posts and Telecommunications（北京邮电大学）
Accurate retinal vessel segmentation is a challenging problem in color fundus image analysis. An automatic retinal vessel segmentation system can effectively facilitate clinical diagnosis and ophthalmological research. Technically, this problem suffers from various degrees of vessel thickness, perception of details, and contextual feature fusion. For addressing these challenges, a deep learning based method has been proposed and several customized modules have been integrated into the well-known encoder-decoder architecture U-net, which is mainly employed in medical image segmentation. Structu…
A Hybrid Framework for Blood Vessel Morphology Classification: Discrete Geometry-based Tortuosity Feature Measurement, Information Gain-based Feature Selection, and Random Forest Classification
North China University of Technology（华北理工大学）；Capital Medical University（首都医科大学）
Pattern Recognition Lab, FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学模式识别实验室）；Department of Ophthalmology, Universitätsklinikum Erlangen（埃尔朗根大学医院眼科）
Ophthalmological imaging utilizes different imaging systems, such as color fundus, infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT angiography. Multiple images with different modalities or acquisition times are often analyzed for the diagnosis of retinal diseases. Automatically aligning the vessel structures in the images by means of multi-modal registration can support the ophthalmologists in their work. Our method uses a convolutional neural network to extract features of the vessel structure in multi-modal retinal images. We jointly train a keypoint detection an…
Heritage Institute of Technology（遗产理工学院）；Tata Consultancy Services（塔塔咨询服务公司）
Diabetic Retinopathy (DR) is a complication of long-standing, unchecked diabetes and one of the leading causes of blindness in the world. This paper focuses on improved and robust methods to extract some of the features of DR, viz. Blood Vessels and Exudates. Blood vessels are segmented using multiple morphological and thresholding operations. For the segmentation of exudates, k-means clustering and contour detection on the original images are used. Extensive noise reduction is performed to remove false positives from the vessel segmentation algorithm's results. The localization of Optic Disc …
Retinal blood vessel can assist doctors in diagnosis of eye-related diseases such as diabetes and hypertension, and its segmentation is particularly important for automatic retinal image analysis. However, it is challenging to segment these vessels structures, especially the thin capillaries from the color retinal image due to low contrast and ambiguousness. In this paper, we propose pyramid U-Net for accurate retinal vessel segmentation. In pyramid U-Net, the proposed pyramid-scale aggregation blocks (PSABs) are employed in both the encoder and decoder to aggregate features at higher, current…
Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation
University of Twente（特文特大学）；Amsterdam UMC location Vrije Universiteit Amsterdam（阿姆斯特丹大学医学中心（阿姆斯特丹自由大学医学中心））；Amsterdam Cardiovascular Sciences（阿姆斯特丹心血管科学研究院）
Coronary angiography is the "gold standard" for diagnosing coronary artery disease (CAD). At present, the methods for detecting and evaluating coronary artery stenosis cannot satisfy the clinical needs, e.g., there is no prior study of detecting stenoses in prespecified vessel segments, which is necessary in clinical practice. Two vascular stenosis detection methods are proposed to assist the diagnosis. The first one is an automatic method, which can automatically extract the entire coronary artery tree and mark all the possible stenoses. The second one is an interactive method. With this meth…
School of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）；School of Material Science and Engineering, Ocean University of China（中国海洋大学材料科学与工程学院）；School of Mathematical Sciences, Ocean University of China（中国海洋大学数学科学学院）；Department of Cardiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University（山东大学齐鲁医学院齐鲁医院（青岛）心内科）
Coronary angiography is the "gold standard" for diagnosing coronary artery disease (CAD). At present, the methods for detecting and evaluating coronary artery stenosis cannot satisfy the clinical needs, e.g., there is no prior study of detecting stenoses in prespecified vessel segments, which is necessary in clinical practice. Two vascular stenosis detection methods are proposed to assist the diagnosis. The first one is an automatic method, which can automatically extract the entire coronary artery tree and mark all the possible stenoses. The second one is an interactive method. With this meth…
Quantification of cardiac structures in non-contrast CT (NCCT) could improve cardiovascular risk stratification. However, setting a manual reference to train a fully convolutional network (FCN) for automatic segmentation of NCCT images is hardly feasible, and an FCN trained on coronary CT angiography (CCTA) images would not generalize to NCCT. Therefore, we propose to train an FCN with virtual non-contrast (VNC) images from a dual-layer detector CT scanner and a reference standard obtained on perfectly aligned CCTA images.
CMEMS-UMinho Research Unit（米尼奥大学CMEMS研究中心）；University of Minho（米尼奥大学）
The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monitor them. In this paper, we propose a novel method that combines the multiscale analysis provided by the Stationary Wavelet Transform with a multiscale Fully Convolutional Neural Network to cope with the varying width and direction of the vessel structure in the retina. Our proposal uses rotation operations as the basis of a joint strategy for both data augmentation and prediction, which allows us to explore the inform…
Accurate hepatic vessel segmentation on ultrasound (US) images can be an important tool in the planning and execution of surgery, however proves to be a challenging task due to noise and speckle. Our method comprises a reduced filter 3D U-Net implementation to automatically detect hepatic vasculature in 3D US volumes. A comparison is made between volumes acquired with a 3D probe and stacked 2D US images based on electromagnetic tracking. Experiments are conducted on 67 scans, where 45 are used in training, 12 in validation and 10 in testing. This network architecture yields Dice scores of 0.74…
Accurate segmentation of cerebral vasculature and a quantitative assessment of cerebrovascular morphology is critical to various diagnostic and therapeutic purposes and is pertinent to studying brain health and disease. However, this is still a challenging task due to the complexity of the vascular imaging data. We propose an automated method for cerebral vascular segmentation without the need of any manual intervention as well as a method to skeletonize the binary volume to extract vascular geometric features which can characterize vessel structure. We combine a probabilistic vessel-enhancing…
