Florian Thamm, Felix Denzinger, Leonhard Rist, Celia Martin Vicario, Florian Kordon, Andreas Maier
In this report we want to present our method and results for the Carotid Artery Vessel Wall Segmentation Challenge. We propose an image-based pipeline utilizing the U-Net architecture and location priors to solve the segmentation problem at hand.
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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…
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…
Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation
针对多序列MRI颈动脉分割中标记数据稀缺和斑块形态复杂的问题，提出一种结合粗定位与精细分割、多级多序列U-Net融合及半监督一致性约束的深度学习方法，在52名患者数据上验证了有效性。
Segmentation of carotid vessel wall is required in vessel wall volume (VWV) and local vessel-wall-plus-plaque thickness (VWT) quantification of the carotid artery. Manual segmentation of the vessel wall is time-consuming and prone to interobserver variability. In this paper, we proposed a convolution neural network to segment the common carotid artery (CCA) from 3D carotid ultrasound images. The proposed CNN involves three U-Nets that segmented the 3D ultrasound (3DUS) images in the axial, lateral and frontal orientations. The segmentation maps generated by three U-Nets were consolidated by a …
An automated framework for brain vessel centerline extraction from CTA images
本文提出一种从CTA图像自动提取脑血管中心线的框架，通过无需医生额外标注的管腔分割生成和双分支拓扑感知UNet（DTUNet）提升中心线提取性能，并在多中心数据集上优于现有方法。
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…
Uncertainty-based quality assurance of carotid artery wall segmentation in black-blood MRI
本文评估Monte Carlo dropout与测试时数据增强所估计的分割不确定性，证明其可在不降低颈动脉壁分割质量的前提下检测低质量结果。
CaroTo: A Tool for Fast Comprehensive Analysis of Carotid Artery Stenosis in 4D PC- and 3D BB-MRI Data
CaroTo工具通过多模态和多维分割、生物标志物提取和可视化，实现颈动脉动脉粥样斑块的标准化评估，提升颈动脉狭窄分析的精度和一致性。
GAPNet: Granularity Attention Network with Anatomy-Prior-Constraint for Carotid Artery Segmentation
本文提出GAPNet，通过引入解剖先验约束和粒度注意力机制，解决磁共振黑血血管壁成像中颈动脉因颈部解剖复杂而难以准确分割的问题。
This study is to characterize reflectance profiles of retinal blood vessels in optical coherence tomography (OCT), and to validate these vascular features to guide artery-vein classification in OCT angiography (OCTA) of human retina. Depth-resolved OCT reveals unique features of retinal arteries and veins. Retinal arteries show hyper-reflective boundaries at both upper (inner side towards the vitreous) and lower (outer side towards the choroid) walls. In contrary, retinal veins reveal hyper-reflectivity at the upper boundary only. Uniform lumen intensity was observed in both small and large ar…
Objective: Vessel-wall-volume (VWV) and localized vessel-wall-thickness (VWT) measured from 3D ultrasound (US) carotid images are sensitive to anti-atherosclerotic effects of medical/dietary treatments. VWV and VWT measurements require the lumen-intima (LIB) and media-adventitia boundaries (MAB) at the common and internal carotid arteries (CCA and ICA). However, most existing segmentation techniques were capable of automating only CCA segmentation. An approach capable of segmenting the MAB and LIB from the CCA and ICA was required to accelerate VWV and VWT quantification. Methods: Segmentation…
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…
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…
Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction. However, the existing methods do not consider the characteristics of multi-coil acquisition of MRI data. Therefore, we give a new diffusion model, called SPIRiT-Diffusion, based on the SPIRiT iterative reconstruction algorithm. Specifically, SPIRiT-Diffusion characterizes the prior distribution of coil-by-coil images by score matching and characterizes the k-space redundant prior between coils based on self-consistency. With sufficient prior constraint utilized, we achieve s…
A CUBS-Compatible Ultrasound Morphology and Uncertainty-Aware Baseline for Carotid Intima-Media Segmentation and Preliminary Risk Prediction
本文提出AtheroFlow-XNet模型，通过超声图像分割和风险预测，利用CUBS数据集进行训练，结合临床变量和不确定性推理，实现颈动脉形态分析和风险评估。
The first choice in diagnostic imaging for patients suffering from peripheral arterial disease is 2D ultrasound (US). However, for a proper imaging process, a skilled and experienced sonographer is required. Additionally, it is a highly user-dependent operation. A robotized US system that autonomously scans the peripheral arteries has the potential to overcome these limitations. In this work, we extend a previously proposed system by a hierarchical image analysis pipeline based on convolutional neural networks in order to control the robot. The system was evaluated by checking its feasibility …
Intravascular Ultrasound (IVUS) is an intra-operative imaging modality that facilitates observing and appraising the vessel wall structure of the human coronary arteries. Segmentation of arterial wall boundaries from the IVUS images is not only crucial for quantitative analysis of the vessel walls and plaque characteristics, but is also necessary for generating 3D reconstructed models of the artery. The aim of this study is twofold. Firstly, we investigate the feasibility of using a recently proposed region detector, namely Extremal Region of Extremum Level (EREL) to delineate the luminal and …
VAST: Vascular Flow Analysis and Segmentation for Intracranial 4D Flow MRI
VAST通过自动化处理和物理约束，提高了颅内4D血流MRI的精度和实用性，实现了更一致的血流场分析。
High Accuracy Pulmonary Vessel Segmentation for Contrast and Non-contrast CT Images and Clinical Evaluation
本研究提出一种含VLSOM模块与Cl-Dice Loss的3D肺血管分割算法，支持增强与非增强CT图像，在多源数据上取得高精度分割效果，临床评估表现优异，具备临床应用潜力。
IntraVascular UltraSound (IVUS) is one of the most effective imaging modalities that provides assistance to experts in order to diagnose and treat cardiovascular diseases. We address a central problem in IVUS image analysis with Fully Convolutional Network (FCN): automatically delineate the lumen and media-adventitia borders in IVUS images, which is crucial to shorten the diagnosis process or benefits a faster and more accurate 3D reconstruction of the artery. Particularly, we propose an FCN architecture, called IVUS-Net, followed by a post-processing contour extraction step, in order to autom…
Detection of coronary artery stenosis in coronary CT angiography (CCTA) requires highly personalized surface meshes enclosing the coronary lumen. In this work, we propose to use graph convolutional networks (GCNs) to predict the spatial location of vertices in a tubular surface mesh that segments the coronary artery lumen. Predictions for individual vertex locations are based on local image features as well as on features of neighboring vertices in the mesh graph. The method was trained and evaluated using the publicly available Coronary Artery Stenoses Detection and Quantification Evaluation …
The purpose of this study is to present a new semi-automated methodology for three-dimensional (3D) reconstruction of coronary arteries and their plaque morphology using Computed Tomography Angiography (CTA) images. The methodology is summarized in seven stages: pre-processing of the acquired CTA images, extraction of the vessel tree centerline, estimation of a weight function for lumen, outer wall and calcified plaque, lumen segmentation, outer wall segmentation, plaque detection, and finally 3D surfaces construction. The methodology was evaluated using both expert manual annotations and esti…
TG-OT: Topology-guided CCTA-IVUS registration via optimal transport matching
提出TG-OT框架，通过集成训练的特征检测器和基于不平衡Sinkhorn最优传输损失的拓扑圆柱配准，实现CCTA与IVUS的全自动配准，无需预分割，在47例数据上取得高精度。
Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation
本文提出脉搏辅助分割神经网络PAS-NN，利用运动放大技术提取超声图像中的心脏诱导脉搏信号作为注意力引导，并结合机械臂稳定扫描，有效提升了颈动脉和桡动脉的分割精度与稳定性。
Force Sensing Guided Artery-Vein Segmentation via Sequential Ultrasound Images
本文提出一种力感知引导的超声动静脉分割方法，利用血管形变关键帧与注意力机制提升分割精度，并构建了首个多模态超声动静脉分割数据集Mus-V。
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。
Airway segmentation is critical for virtual bronchoscopy and computer-aided pulmonary disease analysis. In recent years, convolutional neural networks (CNNs) have been widely used to delineate the bronchial tree. However, the segmentation results of the CNN-based methods usually include many discontinuous branches, which need manual repair in clinical use. A major reason for the breakages is that the appearance of the airway wall can be affected by the lung disease as well as the adjacency of the vessels, while the network tends to overfit to these special patterns in the training set. To lear…
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