A multi-backbone cascade and morphology-aware segmentation network for complex morphological X-ray coronary artery images

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Computerized Medical Imaging and Graphics Pub Date : 2025-08-14 DOI:10.1016/j.compmedimag.2025.102629

Xiaodong Zhou , Huibin Wang , Lili Zhang

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引用次数: 0

Abstract

X-ray coronary artery images are the ‘gold standard’ technology for diagnosing coronary artery disease, but due to the complex morphology of the coronary arteries, such as overlapping, winding and uneven contrast media filling, the existing segmentation methods often suffer from segmentation errors and vessel breakage. To this end, we proposed a multi-backbone cascade and morphology-aware segmentation network (MBCMA-Net), which improves the feature extraction ability of the network through multi-backbone encoders, and embeds a vascular morphology-aware module in the backbone network to enhance the capability of complex structure recognition, and finally introduces a centerline loss function to maintain the vascular connectivity. During the experiment, we selected 1942 clear angiograms from two public datasets (DCA1¹ and CADICA²) and annotated them, and also used the public ARCADE³ dataset for testing. Experimental results show that MBCMA-Net reaches an IoU of 87.14%, a DSC score of 92.72%, and a vascular connectivity score of 89.05%, which is better than the mainstream segmentation algorithms and can be used as a benchmark model for coronary artery segmentation.

Code repository: https://gitee.com/zaleman/mbcma-net.

查看原文本刊更多论文

基于多主干级联和形态感知的冠状动脉x线图像分割网络

冠状动脉x线图像是诊断冠状动脉疾病的“金标准”技术，但由于冠状动脉形态复杂，如重叠、缠绕、造影剂填充不均等，现有的分割方法往往存在分割错误和血管破裂的问题。为此，我们提出了一种多骨干级联和形态感知分割网络（MBCMA-Net），通过多骨干编码器提高网络的特征提取能力，并在骨干网络中嵌入血管形态感知模块以增强复杂结构识别能力，最后引入中心线损失函数以保持血管连通性。实验中，我们从两个公开数据集（DCA11和CADICA2）中选取1942张清晰血管图进行标注，并使用公开的ARCADE3数据集进行测试。实验结果表明，MBCMA-Net的IoU为87.14%，DSC评分为92.72%，血管连通性评分为89.05%，优于主流分割算法，可作为冠状动脉分割的基准模型。代码存储库：https://gitee.com/zaleman/mbcma-net。

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来源期刊

Computerized Medical Imaging and Graphics 医学-核医学

CiteScore

10.70

自引率

3.50%

发文量

审稿时长

26 days

期刊介绍： The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.