Recent advances in 3D imaging technologies provide novel insights to researchers and reveal finer and more detail of examined specimen, especially in the biomedical domain, but also impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing dataset sizes. In particular, existing research towards automated vessel network analysis does not consider memory requirements of proposed algorithms and often generates a large number of spurious branches for structures consisting of many voxels. Additionally, very often these algorithms have further restrictions such as the limitation to tree topologies or relying on the properties of specific image modalities. We present a scalable pipeline (in terms of computational cost, required main memory and robustness) that extracts an annotated abstract graph representation from the foreground segmentation of vessel networks of arbitrary topology and vessel shape. Only a single, dimensionless, a-priori determinable parameter is required. By careful engineering of individual pipeline stages and a novel iterative refinement scheme we are, for the first time, able to analyze the topology of volumes of roughly 1TB on commodity hardware. An implementation of the presented pipeline is publicly available in version 5.1 of the volume rendering and processing engine Voreen (https://www.uni-muenster.de/Voreen/).
Seoul Nat’l Univ.（首尔国立大学）；Soonchunhyang Univ.（顺天乡大学）；Hankuk Univ. of Foreign Studies（韩国外国语大学）
We present a method to extract coronary vessels from fluoroscopic x-ray sequences. Given the vessel structure for the source frame, vessel correspondence candidates in the subsequent frame are generated by a novel hierarchical search scheme to overcome the aperture problem. Optimal correspondences are determined within a Markov random field optimization framework. Post-processing is performed to extract vessel branches newly visible due to the inflow of contrast agent. Quantitative and qualitative evaluation conducted on a dataset of 18 sequences demonstrates the effectiveness of the proposed …
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）
FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures
Skolkovo Institute of Science and Technology (Skoltech)（斯科尔科沃科学技术研究院（斯科尔泰克））；Artificial Intelligence Research Institute (AIRI)（人工智能研究院（AIRI））；University of Sharjah（沙迦大学）；Bian Que AI（扁鹊人工智能）；University College London（伦敦大学学院）；Research Institute for Medical and Health Sciences（医学与健康科学研究院）
ImageCAS-X: a dataset and benchmark for coronary artery segmentation and centerline extraction in coronary CT angiography
DTU Compute, Technical University of Denmark（丹麦技术大学DTU计算学院）；Cardiovascular Research Unit, Copenhagen University Hospital – Rigshospitalet（哥本哈根大学医院——国立医院心血管研究科）
Nanjing University of Science and Technology（南京理工大学）；The First Affiliated Hospital with Nanjing Medical University（南京医科大学第一附属医院）
Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging modality that allows micron-level resolution to visualize the retinal microvasculature. The retinal vessel segmentation in OCTA images is still an open problem, and especially the thin and dense structure of the capillary plexus is an important challenge of this problem. In this work, we propose a novel image magnification network (IMN) for vessel segmentation in OCTA images. Contrary to the U-Net structure with a down-sampling encoder and up-sampling decoder, the proposed IMN adopts the design of up-sampling encod…
Assessing Pancreatic Ductal Adenocarcinoma Vascular Invasion: the PDACVI Benchmark
Sycai Technologies SL, Scientific；Technical Department, Barcelona, Spain；Universit\" a tsklinikum Erlangen, Department of Radiology of the Uniklinikum Erlangen (UKER), Erlangen, Germany；University Hospital Erlangen, Imaging Science Institute, Erlangen, Germany；ICUBE Laboratory, CNRS UMR-7357, University of Strasbourg, Strasbourg, France；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China；University of Chinese Academy of Sciences, Beijing, China；German Cancer Research Center (DKFZ), Medical Image Computing (E230), Heidelberg, Germany；Hospital de Matar\' o , Matar\' o , Spain；Hospital de Sant Pau i la Santa Creu, Diagnostic Imaging Department, Barcelona, Spain；Institut de Recerca Sant Pau - CERCA, Advanced Medical Imaging, Artificial Intelligence；Imaging-Guided Therapy Research Group, Barcelona, Spain；TECNALIA, Basque Research
BioVessel-Net and RetinaMix: Unsupervised Retinal Vessel Segmentation from OCTA Images
University of Electronic Science and Technology of China（电子科技大学）；Southern Methodist University（南方法律大学）；University of Texas Southwestern Medical Center（德克萨斯西南医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Georgetown University（杰斐逊大学）；Sichuan Provincial People’s Hospital（四川省级人民医院）；University of Connecticut（康涅狄格大学）
Shanghai Jiao Tong University（上海交通大学）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院胰腺疾病研究所）
The extraction of contrast-filled vessels from X-ray coronary angiography (XCA) image sequence has important clinical significance for intuitively diagnosis and therapy. In this study, the XCA image sequence is regarded as a 3D tensor input, the vessel layer is regarded as a sparse tensor, and the background layer is regarded as a low-rank tensor. Using tensor nuclear norm (TNN) minimization, a novel method for vessel layer extraction based on tensor robust principal component analysis (TRPCA) is proposed. Furthermore, considering the irregular movement of vessels and the low-frequency dynamic…
VEELA: A Clinically-Constrained Benchmark for Liver Vessel Segmentation in Computed Tomography Angiography
Department of Radiology, Dokuz Eylul University（多尔朱·伊勒大学放射科）；Department of Computer Engineering, Istanbul Technical University（伊斯坦布尔技术大学计算机工程系）；Institute of Natural and Applied Sciences, Dokuz Eylul University（多尔朱·伊勒大学自然科学与应用科学学院）；Department of Electrical and Electronics Engineering, Dokuz Eylul University（多尔朱·伊勒大学电气与电子工程系）；Department of Radiology, University of Wisconsin-Madison（威斯康星大学麦迪逊分校放射科）；School of Medicine, Sivas Cumhuriyet University（萨瓦斯·库尔德大学医学院）；School of Medicine, Acibadem Mehmet Ali Aydinlar University（阿克塞姆·梅赫梅特·阿里·阿迪姆大学医学院）；Department of Artificial Intelligence Engineering, Bahçeşehir University（巴切希尔大学人工智能工程系）；Faculty of Pharmacy, Sivas Cumhuriyet University（萨瓦斯·库尔德大学药学院）
Temporally Consistent Graph Extraction and Matching for Longitudinal Angiographic Images
Technical University of Munich (TUM)（慕尼黑工业大学）；TUM University Hospital（慕尼黑工业大学附属医院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Imperial College London（伦敦帝国理工学院）
CUHKSZ（香港中文大学（深圳））；Tencent America（腾讯美国）；Technical University of Munich（慕尼黑工业大学）
Extracting parametric edge curves from point clouds is a fundamental problem in 3D vision and geometry processing. Existing approaches mainly rely on keypoint detection, a challenging procedure that tends to generate noisy output, making the subsequent edge extraction error-prone. To address this issue, we propose to directly detect structured edges to circumvent the limitations of the previous point-wise methods. We achieve this goal by presenting NerVE, a novel neural volumetric edge representation that can be easily learned through a volumetric learning framework. NerVE can be seamlessly co…
Improve Retinal Artery/Vein Classification via Channel Couplin
Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China（北京大学医学部医学技术研究所）；Department of Biomedical Engineering, Peking University, Beijing, China（北京大学生物医学工程系）；National Biomedical Imaging Center, Peking University, Beijing, China（北京大学国家生物医学成像中心）；Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology（佐治亚理工学院生物医学工程系）；Emory University, Atlanta, GA, USA（埃默里大学）
Deepwise AI Lab（深度智耀人工智能实验室）；Northwestern Polytechnical University（西北工业大学）；Peking University（北京大学）
Analyzing the morphological attributes of blood vessels plays a critical role in the computer-aided diagnosis of many cardiovascular and ophthalmologic diseases. Although being extensively studied, segmentation of blood vessels, particularly thin vessels and capillaries, remains challenging mainly due to the lack of an effective interaction between local and global features. In this paper, we propose a novel deep learning model called PC-Net to segment retinal vessels and major arteries in 2D fundus image and 3D computed tomography angiography (CTA) scans, respectively. In PC-Net, the pyramid …
DeformCL: Learning Deformable Centerline Representation for Vessel Extraction in 3D Medical Image
Yizhun Medical AI Co., Ltd（亿智臻医疗人工智能有限公司）；Center for Data Science, Peking University（北京大学数据科学中心）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）；State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（北京大学人工智能国家重点实验室）；Pazhou Laboratory (Huangpu), Guangzhou, Guangdong, China（琶洲实验室（黄埔），广州，广东，中国）
Peking University（北京大学）；The Hong Kong University of Science and Technology（香港科技大学）；Deepwise AI Lab（深智城人工智能实验室）
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 re…
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…
The health and function of tissue rely on its vasculature network to provide reliable blood perfusion. Volumetric imaging approaches, such as multiphoton microscopy, are able to generate detailed 3D images of blood vessels that could contribute to our understanding of the role of vascular structure in normal physiology and in disease mechanisms. The segmentation of vessels, a core image analysis problem, is a bottleneck that has prevented the systematic comparison of 3D vascular architecture across experimental populations. We explored the use of convolutional neural networks to segment 3D ves…
University of Maryland, College Park（马里兰大学帕克分校）；Intuitive Surgical, Inc（直观外科公司）
Semantic image segmentation plays an important role in modeling patient-specific anatomy. We propose a convolution neural network, called Kid-Net, along with a training schema to segment kidney vessels: artery, vein and collecting system. Such segmentation is vital during the surgical planning phase in which medical decisions are made before surgical incision. Our main contribution is developing a training schema that handles unbalanced data, reduces false positives and enables high-resolution segmentation with a limited memory budget. These objectives are attained using dynamic weighting, ran…
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Continuing Education, Shanghai Jiao Tong University（上海交通大学继续教育学院）；Shanghai Jiao Tong University Affiliated Sixth People’s Hospital（上海交通大学附属第六人民医院）；Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院）；Center for Advanced Neuroimaging, University of California, Riverside（加州大学河滨分校高级神经影像中心）
Although robust PCA has been increasingly adopted to extract vessels from X-ray coronary angiography (XCA) images, challenging problems such as inefficient vessel-sparsity modelling, noisy and dynamic background artefacts, and high computational cost still remain unsolved. Therefore, we propose a novel robust PCA unrolling network with sparse feature selection for super-resolution XCA vessel imaging. Being embedded within a patch-wise spatiotemporal super-resolution framework that is built upon a pooling layer and a convolutional long short-term memory network, the proposed network can not onl…
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）
There are various methods for extracting iso-surfaces from volumetric data. Marching cubes or tetrahedra or raytracing methods are mostly used. There are many specific techniques to increase speed of computation and decrease memory requirements. Although a precision of iso-surface extraction is very important, too, it is not mentioned usually. A comparison of the selected methods was made in different aspects: iso-surface extraction process time, number of triangles generated and estimation of radius, area and volume errors based on approximation of a sphere. Surprisingly, experiments proved t…
Tel Aviv University（特拉维夫大学）；Facebook AI Research（脸书人工智能研究院）
Recently developed methods for rapid continuous volumetric two-photon microscopy facilitate the observation of neuronal activity in hundreds of individual neurons and changes in blood flow in adjacent blood vessels across a large volume of living brain at unprecedented spatio-temporal resolution. However, the high imaging rate necessitates fully automated image analysis, whereas tissue turbidity and photo-toxicity limitations lead to extremely sparse and noisy imagery. In this work, we extend a recently proposed deep learning volumetric blood vessel segmentation network, such that it supports …
Beihang University（北京航空航天大学）；International Research Institute for Multidisciplinary Science, Beihang University（北京航空航天大学国际交叉科学研究院）
Voxel is an important format to represent geometric data, which has been widely used for 3D deep learning in shape analysis due to its generalization ability and regular data format. However, fine-grained tasks like part segmentation require detailed structural information, which increases voxel resolution and thus causes other issues such as the exhaustion of computational resources. In this paper, we propose a novel volumetric convolutional neural network, which could extract discriminative features encoding detailed information from voxelized 3D data under a limited resolution. To this purp…
TU München（慕尼黑工业大学）；Klinikum Rechts der Isar（伊萨尔河畔临床医院）
Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modeling. In this work we present a machine learning based feature extraction scheme based on inception models for pixel classification tasks. We extract features under multi-scale and multi-layer schemes through convolutional operators. Layers of Fully Convolutional Network are later stacked on this feature extraction layers and trained end-to-end for the purpose of classification. We test our model on the DRIVE and STARE public data sets for the purpose of segmentation and c…
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）
University of New South Wales（新南威尔士大学）；CSIRO（联邦科学与工业研究组织）；Prince of Wales Hospital（威尔士亲王医院）
Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods to examine three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Se…
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医学机器人研究所）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）；Shanghai AI Lab（上海人工智能实验室）
Automating the extraction and quantification of features from three-dimensional (3-D) image stacks is a critical task for advancing computer vision research. The union of 3-D image acquisition and analysis enables the quantification of biological resistance of a plant tissue to fungal infection through the analysis of attributes such as fungal penetration depth, fungal mass, and branching of the fungal network of connected cells. From an image processing perspective, these tasks reduce to segmentation of vessel-like structures and the extraction of features from their skeletonization. In order…
Novel Extraction of Discriminative Fine-Grained Feature to Improve Retinal Vessel Segmentation
Department of Biomedical Engineering（生物医学工程系）；Peking University（北京大学）；Institute of Medical Technology（医学技术研究所）；Peking University Health Science Center（北京大学医学部）；National Biomedical Imaging Center（国家生物医学成像中心）；Institute of Biomedical Engineering（生物医学工程研究所）；Peking University Shenzhen Graduate School（北京大学深圳研究生院）；school of Electrical and Computer Engineering（电气与计算机工程学院）；Georgia Institute of Technology（佐治亚理工学院）；Peter O’Donnell Jr. School of Public Health（公共健康学院）；at UT Southwestern Medical Center (UTSW)（在德克萨斯西南医学中心 (UTSW)）；Wallace H. Coulter Department of Biomedical Engineering（Wallace H. Coulter 生物医学工程系）；Georgia Institute of Technology and Emory University（佐治亚理工学院和埃默里大学）
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 …
The Chinese University of Hong Kong（香港中文大学）；Chang Gung University（长庚大学）；The Hong Kong Polytechnic University（香港理工大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected volumetric convolutional neural network, referred as DenseVoxNet, to automatically segment the cardiac and vascular structures from 3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional ar…
HTC-SGA Former: A Hybrid Transformer-CNN Network with Self-Guided Attention and a New Boundary-Weighted Adaptive Loss for Coronary DSA Vessel Segmentation
Shenzhen Institutes of Advanced Technology (SIAT)（深圳先进技术研究院）；University of Chinese Academy of Sciences (UCAS)（中国科学院大学）；Department of Biomedical Engineering and Systems, Faculty of Engineering（工程学院生物医学工程与系统系）；Cairo University（开罗大学）
Optical coherence tomography (OCT) is a non-invasive imaging technique widely used for ophthalmology. It can be extended to OCT angiography (OCT-A), which reveals the retinal vasculature with improved contrast. Recent deep learning algorithms produced promising vascular segmentation results; however, 3D retinal vessel segmentation remains difficult due to the lack of manually annotated training data. We propose a learning-based method that is only supervised by a self-synthesized modality named local intensity fusion (LIF). LIF is a capillary-enhanced volume computed directly from the input OC…
University Paris Dauphine, PSL Research University（巴黎多芬大学，PSL研究大学）；CNRS（法国国家科学研究中心）
In this paper, we propose a new minimal path model for minimally interactive retinal vessel centerline extraction. The main contribution lies at the construction of a novel coherence-penalized Riemannian metric in a lifted space, dependently of the local geometry of tubularity and an external scalar-valued reference feature map. The globally minimizing curves associated to the proposed metric favour to pass through a set of retinal vessel segments with low variations of the feature map, thus can avoid the short branches combination problem and shortcut problem, commonly suffered by the existin…
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…
City, University of London（伦敦城市大学）；University College London（伦敦大学学院）
In this paper, we present a novel neural network architecture for retinal vessel segmentation that improves over the state of the art on two benchmark datasets, is the first to run in real time on high resolution images, and its small memory and processing requirements make it deployable in mobile and embedded systems. The M2U-Net has a new encoder-decoder architecture that is inspired by the U-Net. It adds pretrained components of MobileNetV2 in the encoder part and novel contractive bottleneck blocks in the decoder part that, combined with bilinear upsampling, drastically reduce the paramete…
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）
retinalysis-vascx: An explainable software toolbox for the extraction of retinal vascular biomarkers
Department of Ophthalmology, Erasmus University Medical Center（埃因霍温大学医学中心眼科系）；Department of Epidemiology, Erasmus University Medical Center（埃因霍温大学医学中心流行病学系）；Department of Ophthalmology, Radboud University Medical Center（拉德堡德大学医学中心眼科系）；Institute of Molecular and Clinical Ophthalmology, University of Basel（巴塞尔大学分子与临床眼科研究所）；Dept. of Computational Biology, University of Lausanne（洛桑大学计算生物学系）；Swiss Institute of Bioinformatics, Lausanne, Switzerland（瑞士生物信息学研究所，洛桑，瑞士）；Dept. of Integrative Biomedical Sciences, University of Cape Town（开普敦大学整合生物医学科学系）
Physics-Informed Learning for Time-Resolved Angiographic Contrast Agent Concentration Reconstruction
Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学计算机科学系模式识别实验室）；Siemens Healthineers AG（西门子医疗股份公司）；Research Campus STIMULATE, University of Magdeburg（马格德堡大学STIMULATE研究园区）；Department of Medical Engineering, University of Magdeburg（马格德堡大学医学工程系）
Wuhan University of Science and Technology（武汉科技大学）；Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences（中国科学院慈溪生物医学工程研究所）；Chinese Academy of Sciences（中国科学院）；UBTech Research, UBTech Robotics Corp Ltd（优必选机器人有限公司优必选研究院）；Southern University of Science and Technology（南方科技大学）
The detection of retinal vessel is of great importance in the diagnosis and treatment of many ocular diseases. Many methods have been proposed for vessel detection. However, most of the algorithms neglect the connectivity of the vessels, which plays an important role in the diagnosis. In this paper, we propose a novel method for retinal vessel detection. The proposed method includes a dense dilated network to get an initial detection of the vessels and a probability regularized walk algorithm to address the fracture issue in the initial detection. The dense dilated network integrates newly pro…
Learning Wall Segmentation in 3D Vessel Trees using Sparse Annotations
Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin（柏林夏里特医学院计算机辅助心血管医学研究所）；Fraunhofer MEVIS（弗劳恩霍夫梅维斯研究所）；DZHK (German Centre for Cardiovascular Research)（德国心血管研究中心）；Physikalisch-Technische Bundesanstalt（德国联邦物理技术研究院）；Medical Center—University of Freiburg, University of Freiburg（弗莱堡大学医学中心、弗莱堡大学）
Spatial Graph Representation and Morphometric Analysis of the Pulmonary Vascular Tree From Computed Tomography Using Multi-Scale Hessian-Based Filter Fusion and TEASAR Skeletonization
Texture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition. On this matter, Gabor wavelets has proven to be a useful technique to characterize distinctive texture patterns. However, most of the approaches used to extract descriptors of the Gabor magnitude space usually fail in representing adequately the richness of detail present into a unique feature vector. In this paper, we propose a new method to enhance the Gabor wavelets process extracting a fractal signature of the magnitude spaces. Each signature is reduced usin…
In this paper an efficient approach for human face recognition based on the use of minutiae points in thermal face image is proposed. The thermogram of human face is captured by thermal infra-red camera. Image processing methods are used to pre-process the captured thermogram, from which different physiological features based on blood perfusion data are extracted. Blood perfusion data are related to distribution of blood vessels under the face skin. In the present work, three different methods have been used to get the blood perfusion image, namely bit-plane slicing and medial axis transform, …
