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arXiv 2307.04472cs.CV

基于部分血管标注的冠状动脉分割：自训练与原型学习

Partial Vessels Annotation-based Coronary Artery Segmentation with Self-training and Prototype Learning

Southeast University（东南大学）；Jinling Hospital, Medical School of Nanjing University（南京大学医学院金陵医院）

Zheng Zhang, Xiaolei Zhang, Yaolei Qi, Guanyu Yang

备注：
Accepted at MICCAI 2023
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AI总结：

针对冠状动脉分割标注成本高的问题，本文提出部分血管标注下的渐进式弱监督学习框架，结合自训练与原型学习，仅用24.29%血管标注即达到与全标注基线相当的主干连续性。

摘要：

在冠状动脉计算机断层扫描血管造影（CCTA）图像上进行冠状动脉分割对临床应用至关重要。由于标注过程需要专业知识且劳动密集，相关标签高效学习算法的需求日益增长。为此，我们基于冠状动脉分割的挑战和临床诊断特点，提出了部分血管标注（PVA）。进一步，我们提出了一种渐进式弱监督学习框架，以在PVA下实现准确分割。首先，我们提出的框架学习血管的局部特征，将知识传播到未标注区域。随后，它利用传播的知识学习全局结构，并纠正传播过程中引入的错误。最后，它利用特征嵌入与特征原型之间的相似性来增强测试输出。在临床数据上的实验表明，我们提出的框架在PVA（24.29%血管）下优于对比方法，并在主干连续性上与使用全标注（100%血管）的基线模型达到了可比的性能。

英文摘要：

Coronary artery segmentation on coronary-computed tomography angiography (CCTA) images is crucial for clinical use. Due to the expertise-required and labor-intensive annotation process, there is a growing demand for the relevant label-efficient learning algorithms. To this end, we propose partial vessels annotation (PVA) based on the challenges of coronary artery segmentation and clinical diagnostic characteristics. Further, we propose a progressive weakly supervised learning framework to achieve accurate segmentation under PVA. First, our proposed framework learns the local features of vessels to propagate the knowledge to unlabeled regions. Subsequently, it learns the global structure by utilizing the propagated knowledge, and corrects the errors introduced in the propagation process. Finally, it leverages the similarity between feature embeddings and the feature prototype to enhance testing outputs. Experiments on clinical data reveals that our proposed framework outperforms the competing methods under PVA (24.29% vessels), and achieves comparable performance in trunk continuity with the baseline model using full annotation (100% vessels).

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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