An Intra- and Cross-frame Topological Consistency Scheme for Semi-supervised Atherosclerotic Coronary Plaque Segmentation
- URL: http://arxiv.org/abs/2501.07850v1
- Date: Tue, 14 Jan 2025 05:23:42 GMT
- Title: An Intra- and Cross-frame Topological Consistency Scheme for Semi-supervised Atherosclerotic Coronary Plaque Segmentation
- Authors: Ziheng Zhang, Zihan Li, Dandan Shan, Yuehui Qiu, Qingqi Hong, Qingqiang Wu,
- Abstract summary: Coronary atherosclerosis Analysis (CAA) 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.
We propose a novel dual-consistency semi-supervised framework that integrates Intra-frame Topological Consistency (ITC) and Cross-frame Topological Consistency (CTC)
Our method surpasses existing semi-supervised methods and approaches the performance of supervised methods on CAA.
- Score: 9.902034502749501
- License:
- Abstract: 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.
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