标题： 半监督动脉粥样硬化冠状动脉斑块分割的帧内和帧间拓扑一致性方案 显示英文标题

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

摘要： 从CT血管造影（CTA）图像中精确分割冠状动脉粥样硬化斑块对于先进的冠状动脉粥样硬化分析（CAA）至关重要，该分析特别依赖于通过曲线平面重建重构的血管横截面图像的分析。由于斑块和血管的边界和结构不清晰，导致当前深度学习模型的表现不足，再加上标注此类复杂数据的固有难度，使得这一任务面临重大挑战。为了解决这些问题，我们提出了一种新颖的双一致性半监督框架，该框架结合了帧内拓扑一致性（ITC）和帧间拓扑一致性（CTC），以利用标记和未标记数据。ITC采用双任务网络同时进行分割掩码和骨骼感知距离变换（SDT）预测，通过一致性约束实现拓扑结构的相似预测，而无需额外标注。同时，CTC使用无监督估计器分析相邻帧的骨骼和边界之间的像素流动，确保空间连续性。在两个CTA数据集上的实验表明，我们的方法超越了现有的半监督方法，并在CAA上接近监督方法的性能。此外，我们的方法在ACDC数据集上的表现也优于其他方法，证明了其泛化能力。 显示英文摘要

摘要： Enhancing the precision of segmenting coronary atherosclerotic plaques from CT Angiography (CTA) images is pivotal for advanced Coronary Atherosclerosis Analysis (CAA), which distinctively relies on the analysis of vessel cross-section images reconstructed via Curved Planar Reformation. This task presents significant challenges due to the indistinct boundaries and structures of plaques and blood vessels, leading to the inadequate performance of current deep learning models, compounded by the inherent difficulty in annotating such complex data. To address these issues, we propose a novel dual-consistency semi-supervised framework that integrates Intra-frame Topological Consistency (ITC) and Cross-frame Topological Consistency (CTC) to leverage labeled and unlabeled data. ITC employs a dual-task network for simultaneous segmentation mask and Skeleton-aware Distance Transform (SDT) prediction, achieving similar prediction of topology structure through consistency constraint without additional annotations. Meanwhile, CTC utilizes an unsupervised estimator for analyzing pixel flow between skeletons and boundaries of adjacent frames, ensuring spatial continuity. Experiments on two CTA datasets show that our method surpasses existing semi-supervised methods and approaches the performance of supervised methods on CAA. In addition, our method also performs better than other methods on the ACDC dataset, demonstrating its generalization.