Semantic segmentation of blood vessels is an important task in medical image analysis, but its progress is often hindered by the scarcity of large annotated datasets and the poor generalization of models across different imaging modalities. A key aspect is the tendency of Convolutional Neural Networks (CNNs) to learn texture-based features, which limits their performance when applied to new domains with different visual characteristics. We hypothesize that leveraging geometric priors of vessel shapes, such as their tubular and branching nature, can lead to more robust and data-efficient models. To investigate this, we introduce VessShape, a methodology for generating large-scale 2D synthetic datasets designed to instill a shape bias in segmentation models. VessShape images contain procedurally generated tubular geometries combined with a wide variety of foreground and background textures, encouraging models to learn shape cues rather than textures. We demonstrate that a model pre-trained on VessShape images achieves strong few-shot segmentation performance on two real-world datasets from different domains, requiring only four to ten samples for fine-tuning. Furthermore, the model exhibits notable zero-shot capabilities, effectively segmenting vessels in unseen domains without any target-specific training. Our results indicate that pre-training with a strong shape bias can be an effective strategy to overcome data scarcity and improve model generalization in blood vessel segmentation.
Technion-IIT（以色列理工学院）；KU Leuven（鲁汶大学）；University Hospitals UZ Leuven（鲁汶大学附属医院）
Introduction: Blood vessels can be non-invasively visualized from a digital fundus image (DFI). Several studies have shown an association between cardiovascular risk and vascular features obtained from DFI. Recent advances in computer vision and image segmentation enable automatising DFI blood vessel segmentation. There is a need for a resource that can automatically compute digital vasculature biomarkers (VBM) from these segmented DFI. Methods: In this paper, we introduce a Python Vasculature BioMarker toolbox, denoted PVBM. A total of 11 VBMs were implemented. In particular, we introduce new…
Vector Institute（矢量研究所）；University of Toronto（多伦多大学）；CIFAR（加拿大高等研究院）
This work explores the use of computer vision for image segmentation and classification of medical fluid samples in transparent containers (for example, tubes, syringes, infusion bags). Handling fluids such as infusion fluids, blood, and urine samples is a significant part of the work carried out in medical labs and hospitals. The ability to accurately identify and segment the liquids and the vessels that contain them from images can help in automating such processes. Modern computer vision typically involves training deep neural nets on large datasets of annotated images. This work presents a…
VesselSim: learning 3D blood vessel segmentation without expert annotations
Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada（计算机科学与软件工程系，康科迪亚大学，蒙特利尔，加拿大）；Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada（电气与计算机工程系，康科迪亚大学，蒙特利尔，加拿大）
Computationally synthesized blood vessels can be used for training and evaluation of medical image analysis applications. We propose a deep generative model to synthesize blood vessel geometries, with an application to coronary arteries in cardiac CT angiography (CCTA). In the proposed method, a Wasserstein generative adversarial network (GAN) consisting of a generator and a discriminator network is trained. While the generator tries to synthesize realistic blood vessel geometries, the discriminator tries to distinguish synthesized geometries from those of real blood vessels. Both real and syn…
Cairo higher institute for engineering, computer sciences and management（开罗工程、计算机科学与管理高等学院）；Majmaah University（迈加马大学）；high institute of engineering Shock academy Cairo（开罗肖克学院工程高等学院）
Diabetic retinopathy is the basic reason for visual deficiency. This paper introduces a programmed strategy to identify and dispense with the blood vessels. The location of the blood vessels is the fundamental stride in the discovery of diabetic retinopathy because the blood vessels are the typical elements of the retinal picture. The location of the blood vessels can help the ophthalmologists to recognize the sicknesses prior and quicker. The blood vessels recognized and wiped out by utilizing Gobar filter on two freely accessible retinal databases which are STARE and DRIVE. The exactness of …
Shantou University（汕头大学）；South China University of Technology（华南理工大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Soochow University（苏州大学）
In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy utilizing the continuity and extendibility of retinal blood vessels is integrated into the image matting model for blood vessel segmentation. Normally the matting models require the user specified trimap, which separates the input image into three regions manually: the foreground, background and unknown regions. However, since creating a user specified trimap is a tedious and time-consuming task, region features of blood vessels are used to gener…
Enhancement, followed by segmentation, quantification and modelling, of blood vessels in retinal images plays an essential role in computer-aid retinopathy diagnosis. In this paper, we introduce a new vessel enhancement method which is the bowler-hat transform based on mathematical morphology. The proposed method combines different structuring elements to detect innate features of vessel-like structures. We evaluate the proposed method qualitatively and quantitatively, and compare it with the existing, state-of-the-art methods using both synthetic and real datasets. Our results show that the p…
JIS College of Engineering（JIS工程学院）；Kalyani Govt. Engineering College（卡利亚尼政府工程学院）
Segmentation of blood vessels in retinal images provides early diagnosis of diseases like glaucoma, diabetic retinopathy and macular degeneration. Among these diseases occurrence of Glaucoma is most frequent and has serious ocular consequences that can even lead to blindness, if it is not detected early. The clinical criteria for the diagnosis of glaucoma include intraocular pressure measurement, optic nerve head evaluation, retinal nerve fiber layer and visual field defects. This form of blood vessel segmentation helps in early detection for ophthalmic diseases, and potentially reduces the ri…
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.
University of Moratuwa（莫勒图沃大学）；Base Hospital Kebithigollawa（科比蒂戈勒沃基地医院）
The morphology of blood vessels in retinal fundus images is an important indicator of diseases like glaucoma, hypertension and diabetic retinopathy. The accuracy of retinal blood vessels segmentation affects the quality of retinal image analysis which is used in diagnosis methods in modern ophthalmology. Contrast enhancement is one of the crucial steps in any of retinal blood vessel segmentation approaches. The reliability of the segmentation depends on the consistency of the contrast over the image. This paper presents an assessment of the suitability of a recently invented spatially adaptive…
Hangzhou Dianzi University（杭州电子科技大学）；Bauhaus University Weimar（魏玛包豪斯大学）
In this paper, a novel deep learning framework is proposed for temporal super-resolution simulation of blood vessel flows, in which a high-temporal-resolution time-varying blood vessel flow simulation is generated from a low-temporal-resolution flow simulation result. In our framework, point-cloud is used to represent the complex blood vessel model, resistance-time aided PointNet model is proposed for extracting the time-space features of the time-varying flow field, and finally we can reconstruct the high-accuracy and high-resolution flow field through the Decoder module. In particular, the a…
East China Normal University（华东师范大学）；Ryerson University（瑞尔森大学）
Sturge-Weber syndrome (SWS) is a vascular malformation disease, and it may cause blindness if the patient's condition is severe. Clinical results show that SWS can be divided into two types based on the characteristics of scleral blood vessels. Therefore, how to accurately segment scleral blood vessels has become a significant problem in computer-aided diagnosis. In this research, we propose to continuously upsample the bottom layer's feature maps to preserve image details, and design a novel Claw UNet based on UNet for scleral blood vessel segmentation. Specifically, the residual structure is…
ITER, Siksha ‘O’ Anusandhan (Deemed to be University)（ITER，锡克沙·奥·阿努桑德汉（被视为大学））
In the medical domain, different computer-aided diagnosis systems have been proposed to extract blood vessels from retinal fundus images for the clinical treatment of vascular diseases. Accurate extraction of blood vessels from the fundus images using a computer-generated method can help the clinician to produce timely and accurate reports for the patient suffering from these diseases. In this article, we integrate top-hat based preprocessing approach with fine-tuned B-COSFIRE filter to achieve more accurate segregation of blood vessel pixels from the background. The use of top-hat transformat…
Bangladesh University of Engineering and Technology (BUET)（孟加拉国工程技术大学（BUET））；Georgia Institute of Technology（佐治亚理工学院）
Successful identification of blood vessel blockage is a crucial step for Alzheimer's disease diagnosis. These blocks can be identified from the spatial and time-depth variable Two-Photon Excitation Microscopy (TPEF) images of the brain blood vessels using machine learning methods. In this study, we propose several preprocessing schemes to improve the performance of these methods. Our method includes 3D-point cloud data extraction from image modality and their feature-space fusion to leverage complementary information inherent in different modalities. We also enforce the learned representation …
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages of the combination of convolutional neural networks and structured prediction. Our proposed convolutional neural network based model achieves strong performance and significantly outperforms the state…
Federal University of São Carlos（圣卡洛斯联邦大学）；University of Ottawa（渥太华大学）；Ottawa Hospital Research Institute（渥太华医院研究所）
Characterizing blood vessels in digital images is important for the diagnosis of many types of diseases as well as for assisting current researches regarding vascular systems. The automated analysis of blood vessels typically requires the identification, or segmentation, of the blood vessels in an image or a set of images, which is usually a challenging task. Convolutional Neural Networks (CNNs) have been shown to provide excellent results regarding the segmentation of blood vessels. One important aspect of CNNs is that they can be trained on large amounts of data and then be made available, f…
MMM University of Technology（MMM理工大学）；National Institute of Technology, Hamirpur（哈米尔布尔国家理工学院）
Retinal blood vessels structure contains information about diseases like obesity, diabetes, hypertension and glaucoma. This information is very useful in identification and treatment of these fatal diseases. To obtain this information, there is need to segment these retinal vessels. Many kernel based methods have been given for segmentation of retinal vessels but their kernels are not appropriate to vessel profile cause poor performance. To overcome this, a new and efficient kernel based matched filter approach has been proposed. The new matched filter is used to generate the matched filter re…
University of Washington（华盛顿大学）；Kyushu University（九州大学）；National Institute of Informatics（国立信息学研究所）；Keio University（庆应义塾大学）
We propose a fully automatic system to reconstruct and visualize 3D blood vessels in Augmented Reality (AR) system from stereo X-ray images with bones and body fat. Currently, typical 3D imaging technologies are expensive and carrying the risk of irradiation exposure. To reduce the potential harm, we only need to take two X-ray images before visualizing the vessels. Our system can effectively reconstruct and visualize vessels in following steps. We first conduct initial segmentation using Markov Random Field and then refine segmentation in an entropy based post-process. We parse the segmented …
Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images
University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Imperial College London（伦敦帝国学院）；Technical University of Munich（慕尼黑工业大学）；University Hospital Zurich（苏黎世大学医院）；Toronto Western Hospital（多伦多西部医院）；Krembil Research Institute（克雷比尔研究所）
Hierarchical Part-based Generative Model for Realistic 3D Blood Vessel
Shenzhen Key Laboratory of Ubiquitous Data Enabling（Ubiquitous Data Enabling 深圳重点实验室）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Tsinghua University（清华大学）；Pengcheng Laboratory（Pengcheng 实验室）；School of Life Science（生命科学学院）；the Key Laboratory of Convergence Medical Engineering System（融合医学工程系统重点实验室）；Information Technology, Beijing Institute of Technology（信息 technology，北京理工大学）；Institute of Medical Artificial Intelligence（医疗人工智能研究所）；the Second Affiliated Hospital of Xi'an Jiaotong University（西安交通大学第二附属医院）；MoE Key Laboratory of Brain-inspired Intelligent Perception（教育部脑启发智能感知与认知重点实验室）；Cognition, University of Science（认知，科学大学）
Fujian Agriculture and Forestry University（福建农林大学）；Digital Fujian Research Institute of Big Data for Agriculture and Forestry（数字福建农林大数据研究院）
Accurate segmentation of the blood vessels of the retina is an important step in clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and uncertain pathological features make the blood vessel segmentation still very challenging. A novel U-shaped network named Multi-module Concatenation which is based on Atrous convolution and multi-kernel pooling is put forward to retinal vessels segmentation in this paper. The proposed network structure retains three layers the essential stru…
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 …
TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；Department of Computer Science, Boston College（波士顿学院计算机科学系）；Department of Molecular and Cellular Biology, Harvard University（哈佛大学分子与细胞生物学系）
Australian Institute for Machine Learning（澳大利亚机器学习研究所）；The University of Adelaide（阿德莱德大学）；University of Nottingham（诺丁汉大学）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）
Segmentation of cerebral blood vessels from Magnetic Resonance Imaging (MRI) is an open problem that could be solved with deep learning (DL). However, annotated data for training is often scarce. Due to the absence of open-source tools, we aim to develop a classical segmentation method that generates vessel ground truth from Magnetic Resonance Angiography for DL training of segmentation across a variety of modalities. The method combines size-specific Hessian filters, hysteresis thresholding and connected component correction. The optimal choice of processing steps was evaluated with a blinded…
This paper presents a memory efficient architecture that implements the Multi-Scale Line Detector (MSLD) algorithm for real-time retinal blood vessel detection in fundus images on a Zynq FPGA. This implementation benefits from the FPGA parallelism to drastically reduce the memory requirements of the MSLD from two images to a few values. The architecture is optimized in terms of resource utilization by reusing the computations and optimizing the bit-width. The throughput is increased by designing fully pipelined functional units. The architecture is capable of achieving a comparable accuracy to…
Bioinformatics Institute（生物信息学研究所）；Universitat Rovira i Virgili（罗维拉-威尔吉利大学）
In this paper, we propose an efficient blood vessel segmentation method for the eye fundus images using adversarial learning with multiscale features and kernel factorization. In the generator network of the adversarial framework, spatial pyramid pooling, kernel factorization and squeeze excitation block are employed to enhance the feature representation in spatial domain on different scales with reduced computational complexity. In turn, the discriminator network of the adversarial framework is formulated by combining convolutional layers with an additional squeeze excitation block to differe…
Institute of Technical Education & Research, S‘O’A (Deemed to be University)（S‘O’A（被视为大学）技术教育与研究学院）
Blindness in diabetic patients caused by retinopathy (characterized by an increase in the diameter and new branches of the blood vessels inside the retina) is a grave concern. Many efforts have been made for the early detection of the disease using various image processing techniques on retinal images. However, most of the methods are plagued with the false detection of the blood vessel pixels. Given that, here, we propose a modified matched filter with the first derivative of Gaussian. The method uses the top-hat transform and contrast limited histogram equalization. Further, we segment the m…
Segmenting blood vessels in fundus imaging plays an important role in medical diagnosis. Many algorithms have been proposed. While deep Neural Networks have been attracting enormous attention from computer vision community recent years and several novel works have been done in terms of its application in retinal blood vessel segmentation, most of them are based on supervised learning which requires amount of labeled data, which is both scarce and expensive to obtain. We leverage the power of Deep Convolutional Neural Networks (DCNN) in feature learning, in this work, to achieve this ultimate g…
INESC TEC（INESC TEC（葡萄牙系统与计算机工程技术研究所））；Faculty of Sciences of the University of Porto（波尔图大学理学院）
Blood vessel segmentation is one of the most studied topics in computer vision, due to its relevance in daily clinical practice. Despite the evolution the field has been facing, especially after the dawn of deep learning, important challenges are still not solved. One of them concerns the consistency of the topological properties of the vascular trees, given that the best performing methodologies do not directly penalize mistakes such as broken segments and end up producing predictions with disconnected trees. This is particularly relevant in graph-like structures, such as blood vessel trees, …
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…
Recursive Variational Autoencoders for 3D Blood Vessel Generative Modeling
Consejo Nacional de Investigaciones Científicas y Técnicas（阿根廷国家科学与技术研究院）；Universidad Torcuato Di Tella（托科托迪塔拉大学）；Depto. Ing. Eléctrica y Computadoras, Universidad Nacional del Sur（国家南部大学电子工程与计算机系）
Carotid artery vessel wall thickness measurement is an essential step in the monitoring of patients with atherosclerosis. This requires accurate segmentation of the vessel wall, i.e., the region between an artery's lumen and outer wall, in black-blood magnetic resonance (MR) images. Commonly used convolutional neural networks (CNNs) for semantic segmentation are suboptimal for this task as their use does not guarantee a contiguous ring-shaped segmentation. Instead, in this work, we cast vessel wall segmentation as a multi-task regression problem in a polar coordinate system. For each carotid a…
Existing supervised approaches didn't make use of the low-level features which are actually effective to this task. And another deficiency is that they didn't consider the relation between pixels, which means effective features are not extracted. In this paper, we proposed a novel convolutional neural network which make sufficient use of low-level features together with high-level features and involves atrous convolution to get multi-scale features which should be considered as effective features. Our model is tested on three standard benchmarks - DRIVE, STARE, and CHASE databases. The results…
Automated segmentation of vascular map in retinal images endeavors a potential benefit in diagnostic procedure of different ocular diseases. In this paper, we suggest a new unsupervised retinal blood vessel segmentation approach using top-hat transformation, contrast-limited adaptive histogram equalization (CLAHE), and 2-D Gabor wavelet filters. Initially, retinal image is preprocessed using top-hat morphological transformation followed by CLAHE to enhance only the blood vessel pixels in the presence of exudates, optic disc, and fovea. Then, multiscale 2-D Gabor wavelet filters are applied on …
Existing methods to reconstruct vascular structures from a computed tomography (CT) angiogram rely on injection of intravenous contrast to enhance the radio-density within the vessel lumen. However, pathological changes can be present in the blood lumen, vessel wall or a combination of both that prevent accurate reconstruction. In the example of aortic aneurysmal disease, a blood clot or thrombus adherent to the aortic wall within the expanding aneurysmal sac is present in 70-80% of cases. These deformations prevent the automatic extraction of vital clinically relevant information by current m…
Deep Learning Based Estimation of Blood Glucose Levels from Multidirectional Scleral Blood Vessel Imaging
Changsha Aier Eye Hospital（长沙艾尔眼科医院）；Aier Eye Hospital Group Co., Ltd（艾尔眼科医院集团有限公司）；Central South University AIER Eye Institute of Ophthalmology（中南大学AIER眼科院眼科研究所）；Central South University（中南大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
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…
A new dataset for measuring the performance of blood vessel segmentation methods under distribution shifts
Federal University of São Carlos（圣卡洛斯联邦大学）；São Carlos Institute of Physics, University of São Paulo（圣保罗大学圣卡洛斯物理研究所）；University of Ottawa（渥太华大学）；Ottawa Hospital Research Institute（渥太华医院研究所）
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）
A novel topological segmentation of retinal images represents blood vessels as connected regions in the continuous image plane, having shape-related analytic and geometric properties. This paper presents topological segmentation results from the DRIVE retinal image database.
Siksha ‘O’ Anusandhan (Deemed to be University)（锡克沙奥阿努山丹大学（被认定为大学））；ITER, Siksha ‘O’ Anusandhan (Deemed to be University)（锡克沙奥阿努山丹大学（被认定为大学）ITER学院）；Center for the Internet of Things, ITER, Siksha ‘O’ Anusandhan (Deemed to be University)（锡克沙奥阿努山丹大学（被认定为大学）ITER学院物联网中心）
Extracting blood vessels from retinal fundus images plays a decisive role in diagnosing the progression in pertinent diseases. In medical image analysis, vessel extraction is a semantic binary segmentation problem, where blood vasculature needs to be extracted from the background. Here, we present an image enhancement technique based on the morphological preprocessing coupled with a scaled U-net architecture. Despite a relatively less number of trainable network parameters, the scaled version of U-net architecture provides better performance compare to other methods in the domain. We validated…
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；Texas Instruments Inc.（德州仪器公司）
Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large scale screening is the inability to exhaustively detect fine blood vessels crucial to disease diagnosis. In this work we present a computational imaging framework using deep and ensemble learning for reliable detection of blood vessels in fundus color images. An ensemble of deep convolutional neural networks is trained to segment vessel and non-vessel areas of a color fundus image. During inference, the responses of the indi…
