使用动态4D-CTA数据进行3D CTA扫描中鲁棒的自动脑血管分割

在计算机断层扫描血管造影中扩展精细颅内血管标注

Scaling up fine-grained intracranial vessel annotations in computed tomography angiography

提出SemanticVessel数据集，利用4D-CTA动态扫描生成动静脉分割，通过强度引导区域生长标注20种动脉类别，并复用标签扩展数据集，训练模型提升精细分割性能。

VAMOS-OCTA：面向血管的多轴正交监督用于修复运动受损的OCT血管成像体积

VAMOS-OCTA: Vessel-Aware Multi-Axis Orthogonal Supervision for Inpainting Motion-Corrupted OCT Angiography Volumes

VAMOS-OCTA通过多轴正交监督方法提升运动受损OCT血管成像体积的修复效果，增强B扫描锐度和投影准确性。

大血管闭塞性卒中患者的结果预测与个体化治疗效果估计

Outcome prediction and individualized treatment effect estimation in patients with large vessel occlusion stroke

本研究基于449例LVO卒中患者数据，开发可解释深度学习模型，联合临床变量与NCCT/CTA影像预测功能结局并估计个体化治疗效果，实现先进预测性能，但ITE判别能力仍有限。

Synthetic vascular structure generation for unsupervised pre-training in CTA segmentation tasks

Large enough computed tomography (CT) data sets to train supervised deep models are often hard to come by. One contributing issue is the amount of manual labor that goes into creating ground truth labels, specially for volumetric data. In this research, we train a U-net architecture at a vessel segmentation task that can be used to provide insights when treating stroke patients. We create a computational model that generates synthetic vascular structures which can be blended into unlabeled CT scans of the head. This unsupervised approached to labelling is used to pre-train deep segmentation mo…

Stroke Locus Net：基于 MRI 模态的闭塞血管定位

Stroke Locus Net: Occluded Vessel Localization from MRI Modalities

本研究提出端到端深度学习流水线 Stroke Locus Net，结合 nnUNet 分割、动脉图谱与 pGAN 生成，仅用 MRI 扫描实现闭塞血管定位，为更快速的中风诊断提供潜力。

面向心血管疾病治疗的基于人工智能的主动脉血管树分割：现状

AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo

本文系统回顾了主动脉血管树自动与半自动分割的计算技术，并探讨了现有最先进方法在临床实践中的适用性及该研究领域的活跃度。

Serp-Mamba：利用选择性状态空间模型推进高分辨率视网膜血管分割

Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model

针对高分辨率视网膜血管分割，提出基于选择性状态空间模型的Serp-Mamba网络，通过蛇形扫描与模糊驱动双重再校准模块，在三个数据集上取得优越性能。

Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation

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 …

基于生理学合成与域适应的极弱监督血管分割

Extremely weakly-supervised blood vessel segmentation with physiologically based synthesis and domain adaptation

本文针对Micro-CT肾血管标注成本高的问题，提出通过生理学合成血管树并训练生成模型产生对应扫描，再附加分割分支，实现极弱监督下的真实血管分割。

VEELA：一种受临床约束的肝血管分割基准数据集

VEELA: A Clinically-Constrained Benchmark for Liver Vessel Segmentation in Computed Tomography Angiography

本文提出VEELA数据集，用于在CT血管造影中实现肝门静脉分割，通过严格的人工标注和多专家共识，确保标注的临床现实性和准确性，并引入多种评估指标以评估血管分割的多视角性能。

超广角扫频源OCTA数据集与用于视网膜血管分割的极坐标门控Mamba网络

An Ultra-Widefield Swept-Source OCTA Dataset and a Polar-Gated Mamba Network for Retinal Vessel Segmentation

本文提出首个超广角SS-OCTA血管分割数据集WOIVES（206只眼），并设计极坐标门控Mamba网络PG-Mamba，在分割指标上优于七种方法，实现精确的定量血管分析。

Using the Polar Transform for Efficient Deep Learning-Based Aorta Segmentation in CTA Images

Medical image segmentation often requires segmenting multiple elliptical objects on a single image. This includes, among other tasks, segmenting vessels such as the aorta in axial CTA slices. In this paper, we present a general approach to improving the semantic segmentation performance of neural networks in these tasks and validate our approach on the task of aorta segmentation. We use a cascade of two neural networks, where one performs a rough segmentation based on the U-Net architecture and the other performs the final segmentation on polar image transformations of the input. Connected com…

Fast Vessel Segmentation and Tracking in Ultra High-Frequency Ultrasound Images

Ultra High Frequency Ultrasound (UHFUS) enables the visualization of highly deformable small and medium vessels in the hand. Intricate vessel-based measurements, such as intimal wall thickness and vessel wall compliance, require sub-millimeter vessel tracking between B-scans. Our fast GPU-based approach combines the advantages of local phase analysis, a distance-regularized level set, and an Extended Kalman Filter (EKF), to rapidly segment and track the deforming vessel contour. We validated on 35 UHFUS sequences of vessels in the hand, and we show the transferability of the approach to 5 more…

基于深度全卷积网络的CT扫描主动脉血管树分割

Segmentation of Aortic Vessel Tree in CT Scans with Deep Fully Convolutional Networks

针对CT扫描中主动脉血管树分割的复杂性与数据量大的挑战，提出一种两阶段3D全卷积网络方法，通过U型架构与伪中心线分支分割加速医学图像分析，在2023年MICCAI挑战赛中取得DSC为0.920的高精度。

Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction

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…

Automated segmentation of the pulmonary arteries in low-dose CT by vessel tracking

We present a fully automated method for top-down segmentation of the pulmonary arterial tree in low-dose thoracic CT images. The main basal pulmonary arteries are identified near the lung hilum by searching for candidate vessels adjacent to known airways, identified by our previously reported airway segmentation method. Model cylinders are iteratively fit to the vessels to track them into the lungs. Vessel bifurcations are detected by measuring the rate of change of vessel radii, and child vessels are segmented by initiating new trackers at bifurcation points. Validation is accomplished using …

面向CTA与MRA的Willis环拓扑感知探索：分割、检测与分类

Topology-Aware Exploration of Circle of Willis for CTA and MRA: Segmentation, Detection, and Classification

本文提出拓扑感知的统一框架，同时处理CTA和MRA图像，通过通用数据集构建、拓扑感知损失与细化，在TopCow24挑战赛的Willis环分割、检测和分类任务中取得多项领先成绩。

常规血管造影期间腹主动脉中造影剂诱导的CT密度确定性成分：概念验证研究

Contrast-agent-induced deterministic component of CT-density in the abdominal aorta during routine angiography: proof of concept study

本研究基于常规CTA数据建立双sigmoid造影剂动力学模型，验证其可描述正常及病变腹主动脉血流，并服务于图像处理与AI合成数据。

Segmentation of Retinal Blood Vessels Using Deep Learning

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.

An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes

During the diagnosis of ischemic strokes, the Circle of Willis and its surrounding vessels are the arteries of interest. Their visualization in case of an acute stroke is often enabled by Computed Tomography Angiography (CTA). Still, the identification and analysis of the cerebral arteries remain time consuming in such scans due to a large number of peripheral vessels which may disturb the visual impression. In previous work we proposed VirtualDSA++, an algorithm designed to segment and label the cerebrovascular tree on CTA scans. Especially with stroke patients, labeling is a delicate procedu…

Automated Multiscale 3D Feature Learning for Vessels Segmentation in Thorax CT Images

We address the vessel segmentation problem by building upon the multiscale feature learning method of Kiros et al., which achieves the current top score in the VESSEL12 MICCAI challenge. Following their idea of feature learning instead of hand-crafted filters, we have extended the method to learn 3D features. The features are learned in an unsupervised manner in a multi-scale scheme using dictionary learning via least angle regression. The 3D feature kernels are further convolved with the input volumes in order to create feature maps. Those maps are used to train a supervised classifier with t…

TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling

Reconstructing Portal Vein and Hepatic Vein trees from contrast enhanced abdominal CT scans is a prerequisite for preoperative liver surgery simulation. Existing deep learning based methods treat vascular tree reconstruction as a semantic segmentation problem. However, vessels such as hepatic and portal vein look very similar locally and need to be traced to their source for robust label assignment. Therefore, semantic segmentation by looking at local 3D patch results in noisy misclassifications. To tackle this, we propose a novel multi-task deep learning architecture for vessel tree reconstru…

MultiFlow：在多中心单心室患者队列上验证的用于相位对比MRI多血管分类、分割和聚类的统一深度学习框架

MultiFlow: A unified deep learning framework for multi-vessel classification, segmentation and clustering of phase-contrast MRI validated on a multi-site single ventricle patient cohort

提出包含MultiFlowSeg和MultiFlowDTC的深度学习框架，实现单心室患者PCMR数据的多血管自动分类分割与血流表型聚类，处理超5500例检查并发现表型与不良预后显著相关。

Computer Aided Detection of Deep Inferior Epigastric Perforators in Computed Tomography Angiography scans

The deep inferior epigastric artery perforator (DIEAP) flap is the most common free flap used for breast reconstruction after a mastectomy. It makes use of the skin and fat of the lower abdomen to build a new breast mound either at the same time of the mastectomy or in a second surgery. This operation requires preoperative imaging studies to evaluate the branches - the perforators - that irrigate the tissue that will be used to reconstruct the breast mound. These branches will support tissue viability after the microsurgical ligation of the inferior epigastric vessels to the receptor vessels i…

Boundary-Aware Network for Kidney Parsing

Kidney structures segmentation is a crucial yet challenging task in the computer-aided diagnosis of surgery-based renal cancer. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of kidney structures on computed tomography angiography (CTA) images remains challenging, due to the variable sizes of kidney tumors and the ambiguous boundaries between kidney structures and their surroundings. In this paper, we propose a boundary-aware network (BA-Net) to segment kidneys, kidney tumors, arteries, and veins on CTA sc…

ELEMENT：基于耦合区域生长和机器学习方法的多模态视网膜血管分割

ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach

本文提出了一种基于耦合区域生长和机器学习方法的多模态视网膜血管分割框架ELEMENT，通过区域生长和机器学习提取特征并进行像素分类，提高了分割的准确性和效率，实验表明其在多个数据集上均优于现有方法。

VesselSDF：用于血管网络重建的距离场先验

VesselSDF: Distance Field Priors for Vascular Network Reconstruction

针对稀疏CT切片中血管分割的连续性与几何保真难题，提出基于符号距离场回归的VesselSDF框架，并引入自适应高斯正则化消除伪影，显著提升重建精度与连通性。

Retinal OCT Denoising with Pseudo-Multimodal Fusion Network

Optical coherence tomography (OCT) is a prevalent imaging technique for retina. However, it is affected by multiplicative speckle noise that can degrade the visibility of essential anatomical structures, including blood vessels and tissue layers. Although averaging repeated B-scan frames can significantly improve the signal-to-noise-ratio (SNR), this requires longer acquisition time, which can introduce motion artifacts and cause discomfort to patients. In this study, we propose a learning-based method that exploits information from the single-frame noisy B-scan and a pseudo-modality that is c…

面向多中心采集的主动脉血管树自动分割、建模与网格划分：SEG.A. 2023主动脉分割挑战赛综述

Towards the Automatic Segmentation, Modeling and Meshing of the Aortic Vessel Tree from Multicenter Acquisitions: An Overview of the SEG.A. 2023 Segmentation of the Aorta Challenge

本文介绍SEG.A. 2023主动脉分割挑战赛，通过发布多机构公开数据集推动AVT自动分割研究，发现3D U-Net占主导、集成模型性能更优，建立了新基准并提供长期研究资源。

用于冠状动脉标注的Point Transformer

Point Transformer For Coronary Artery Labeling

针对冠状动脉CT血管造影的自动血管标注需求，提出基于Point Transformer的仅需分割结果的标注方法，经53例数据验证有效且易推广至其他血管标注任务。

Label Propagation for 3D Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis

Carotid vessel wall segmentation is a crucial yet challenging task in the computer-aided diagnosis of atherosclerosis. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of carotid vessel wall on magnetic resonance (MR) images remains challenging, due to limited annotations and heterogeneous arteries. In this paper, we propose a semi-supervised label propagation framework to segment lumen, normal vessel walls, and atherosclerotic vessel wall on 3D MR images. By interpolating the provided annotations, we get 3D…

3D convolutional neural network for abdominal aortic aneurysm segmentation

An abdominal aortic aneurysm (AAA) is a focal dilation of the aorta that, if not treated, tends to grow and may rupture. A significant unmet need in the assessment of AAA disease, for the diagnosis, prognosis and follow-up, is the determination of rupture risk, which is currently based on the manual measurement of the aneurysm diameter in a selected Computed Tomography Angiography (CTA) scan. However, there is a lack of standardization determining the degree and rate of disease progression, due to the lack of robust, automated aneurysm segmentation tools that allow quantitatively analyzing the…

DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes

We present DeepVesselNet, an architecture tailored to the challenges faced when extracting vessel networks or trees and corresponding features in 3-D angiographic volumes using deep learning. We discuss the problems of low execution speed and high memory requirements associated with full 3-D convolutional networks, high-class imbalance arising from the low percentage of vessel voxels, and unavailability of accurately annotated training data - and offer solutions as the building blocks of DeepVesselNet. First, we formulate 2-D orthogonal cross-hair filters which make use of 3-D context informat…

Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias

Recent studies suggest that early stages of diabetic retinopathy (DR) can be diagnosed by monitoring vascular changes in the deep vascular complex. In this work, we investigate a novel method for automated DR grading based on optical coherence tomography angiography (OCTA) images. Our work combines OCTA scans with their vessel segmentations, which then serve as inputs to task specific networks for lesion segmentation, image quality assessment and DR grading. For this, we generate synthetic OCTA images to train a segmentation network that can be directly applied on real OCTA data. We test our a…

具有密集-UNet的自监督掩码自动编码器用于有限CT数据中的冠状动脉钙化去除

Self-Supervised Masked Autoencoders with Dense-Unet for Coronary Calcium Removal in limited CT Data

本文提出Dense-MAE，一种基于自监督学习的框架，用于在有限CT数据中有效去除冠状动脉钙化伪影，提升血管狭窄诊断准确性。

多模态视网膜图像的通用血管分割

Universal Vessel Segmentation for Multi-Modality Retinal Images

本文提出通用血管分割模型URVSM，实现多模态视网膜图像的血管分割，首次实现模态无关分割并拓展新模态研究。

一种公开可用的SLO图像血管分割算法

A publicly available vessel segmentation algorithm for SLO images

本文针对IRSLO图像缺乏专用血管分割网络的问题，基于RAVIR及内部标注数据训练U-Net，开发并开源了一种高精度自动分割算法，测试AUC达0.981，便于后续计算分形维数和血管密度等指标。

Vesselpose：从学习的体素方向向量中重建3D血管图像的血管图

Vesselpose: Vessel Graph Reconstruction from Learned Voxel-wise Direction Vectors in 3D Vascular Images

本文提出了一种基于学习的体素方向向量提取拓扑更准确的血管图的方法，通过改进TEASAR算法，在三个基准数据集上实现了最先进的性能，提高了血管图的拓扑准确性。

An application of cascaded 3D fully convolutional networks for medical image segmentation

Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models. To this end, we propose a two-stage, coarse-to-fine approach that will first use a 3D FCN to roughly define a candidate region, which will then be used as input t…

突破数据壁垒：利用基础模型实现鲁棒的少样本3D血管分割

VesselBridge3D: A Foundation Model Adaptation Framework for Label-Efficient 3D Vessel Segmentation

本文提出利用预训练视觉基础模型实现鲁棒的少样本3D血管分割，通过3D适配器和多尺度聚合器提升分割性能，在极端少样本情况下取得显著提升。

基于深度学习的冠状动脉FFR估计

Estimation of FFR in coronary arteries with deep learning

针对传统CFD法估计冠状动脉vFFR耗时且计算成本高的问题，提出将血管几何表示为点云、结合显隐式特征的混合神经网络方法，可在保证质量的同时大幅提升效率，成为临床CFD方案的有力替代。

Dense Residual Network for Retinal Vessel Segmentation

Retinal vessel segmentation plays an imaportant role in the field of retinal image analysis because changes in retinal vascular structure can aid in the diagnosis of diseases such as hypertension and diabetes. In recent research, numerous successful segmentation methods for fundus images have been proposed. But for other retinal imaging modalities, more research is needed to explore vascular extraction. In this work, we propose an efficient method to segment blood vessels in Scanning Laser Ophthalmoscopy (SLO) retinal images. Inspired by U-Net, "feature map reuse" and residual learning, we pro…

一种用于半监督动脉粥样硬化冠状动脉斑块分割的帧内与跨帧拓扑一致性方案

An Intra- and Cross-frame Topological Consistency Scheme for Semi-supervised Atherosclerotic Coronary Plaque Segmentation

针对冠状动脉粥样硬化斑块分割标注难、边界模糊导致模型性能不足的问题，提出整合帧内与跨帧拓扑一致性的半监督框架，在多个数据集上优于现有半监督方法且泛化性良好。

Automatic ultrasound vessel segmentation with deep spatiotemporal context learning

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…

VesselSim: 无需专家标注的3D血管分割学习

VesselSim: learning 3D blood vessel segmentation without expert annotations

提出VesselSim两阶段框架，通过几何驱动的合成血管生成和自监督测试时适应，实现无需真实标注的3D血管分割，在多个临床数据集上达到与有监督方法竞争的性能。

Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection

Ischemic strokes are often caused by large vessel occlusions (LVOs), which can be visualized and diagnosed with Computed Tomography Angiography scans. As time is brain, a fast, accurate and automated diagnosis of these scans is desirable. Human readers compare the left and right hemispheres in their assessment of strokes. A large training data set is required for a standard deep learning-based model to learn this strategy from data. As labeled medical data in this field is rare, other approaches need to be developed. To both include the prior knowledge of side comparison and increase the amoun…

基于血管概率引导衰减学习的稀疏视角动态DSA图像三维血管重建

3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning

提出血管概率引导衰减学习框架，通过静态与动态衰减场互补加权实现稀疏视角DSA重建，降低辐射剂量，并采用渐进训练和时间扰动损失提升质量。

Reference Setup for Quantitative Comparison of Segmentation Techniques for Short Glass Fiber CT Data

Comparing different algorithms for segmenting glass fibers in industrial computed tomography (CT) scans is difficult due to the absence of a standard reference dataset. In this work, we introduce a set of annotated scans of short-fiber reinforced polymers (SFRP) as well as synthetically created CT volume data together with the evaluation metrics. We suggest both the metrics and this data set as a reference for studying the performance of different algorithms. The real scans were acquired by a Nikon MCT225 X-ray CT system. The simulated scans were created by the use of an in-house computational…

基于动态长程依赖与多尺度视网膜边缘融合增强的视网膜血管分割新混合方法

A Novel Hybrid Approach for Retinal Vessel Segmentation with Dynamic Long-Range Dependency and Multi-Scale Retinal Edge Fusion Enhancement

针对现有视网膜血管分割方法在多尺度变异性、复杂曲率等方面的局限，提出结合CNNs与Mamba的混合框架，通过三项创新提升精度，在公开数据集上实现SOTA性能，为临床应用提供鲁棒方法。

通过动脉壁掩膜改善CCTA-CPR扫描中钙化与非钙化斑块的分割

Improving segmentation of calcified and non-calcified plaques on CCTA-CPR scans via masking of the artery wall

本文提出通过动脉壁掩膜减少体素模糊性，利用深度学习血管检测器自动生成内外壁轮廓，从而显著提升CCTA-CPR图像中钙化与非钙化斑块的分割性能。

一种用于半监督肺血管分割的自训练框架及其在COPD中的应用

A Self-training Framework for Semi-supervised Pulmonary Vessel Segmentation and Its Application in COPD

本文提出基于教师-学生模型与可靠伪标签迭代筛选的半监督自训练框架Semi2，用于CT肺血管分割，并在125例COPD患者数据上验证其性能及疾病分析价值。

基于切片一致性布朗桥扩散网络的非增强胸部CT合成增强CT图像生成方法

Generating Synthetic Contrast-Enhanced Chest CT Images from Non-Contrast Scans Using Slice-Consistent Brownian Bridge Diffusion Network

针对造影剂使用风险，本研究提出基于SC-BBDM的非增强CT合成增强CTA图像方法，通过2D高分辨率运算保留3D解剖完整性，在自建数据集上验证了其血管结构保留和对比度保真的优势。

无监督可变形超声图像配准及其在血管分割中的应用

Unsupervised Deformable Ultrasound Image Registration and Its Application for Vessel Segmentation

本文提出无监督可变形超声图像配准模型U-RAFT，基于RAFT改进，无需标注即可生成合成变形图像，用于数据增强，显著提升血管分割的IoU性能，并在体模和猪数据上验证了高SSIM。

基于分割的肿瘤-血管受累评估用于胰腺导管腺癌手术可切除性预测

Segmentation-based Assessment of Tumor-Vessel Involvement for Surgical Resectability Prediction of Pancreatic Ductal Adenocarcinoma

该研究提出基于nnU-Net等分割模型的CT影像工作流程，自动量化PDAC肿瘤与血管接触及受累程度，并用不确定性辅助手术可切除性判断。