Direction- and Centerline-Aware Joint Learning Network (JLNet) for Vessel Segmentation in X-Ray Angiography Images
Direction- and Centerline-Aware Joint Learning Network (JLNet) for Vessel Segmentation in X-Ray Angiography Images
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DOI：
10.1145/3458380.3458383
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
Tao Han;Yonglin Bian;Ruirui An;Yechen Han;Danni Ai;Jian Yang
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文献类型：
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作者：
Tao Han;Yonglin Bian;Ruirui An;Yechen Han;Danni Ai;Jian Yang
从X射线血管造影（XRA）图像中分割血管是冠状动脉疾病临床诊断中的一项重要任务。主要的挑战在于如何从低质量和高复杂度的XRA图像中提取连续和完整的血管掩模。先前的最先进的方法大多基于手工制作的特征或逐像素分割，并且忽略几何特征，从而导致分割掩模中的血管结构的断裂和缺失。在本文中，我们提出了一个几何特征嵌入分割网络的血管分割XRA图像。该网络将方向和中心线相关的预测任务与掩模分割相结合，从而强制网络学习血管连通性的几何特征。此外，提出了一种新的联合损失函数，以促进这三个任务的联合训练。我们在XRA图像上进行消融实验，证明这两个辅助任务可以提高血管分割的连通性和完整性。我们还评估了我们的方法在XRA图像上的血管分割，并实现了85.00 ± 3.66%的值，表明我们的方法优于其他最先进的方法。
Vessel segmentation from X-ray angiography (XRA) images is an important task in the clinical diagnosis of coronary artery disease. The main challenge lies in how to extract continuous and completed vessel masks from XRA images with poor quality and high complexity. Previous state-of-the-art methods are mostly based on hand-crafted features or pixel-wise segmentation and ignore geometric features, thereby resulting in breaks and absence of vessel structure in segmentation masks. In this paper, we propose a geometric feature embedding segmentation network for vessel segmentation in XRA images. This network joins direction- and centerline-related prediction tasks with mask segmentation, which enforces the network to learn the geometric features of vessel connectivity. Besides, a novel joint loss function is proposed to facilitate the joint training of these three tasks. We conduct ablation experiments on XRA images to demonstrate that the two auxiliary tasks can improve the connectivity and completeness of vessel segmentation. We also evaluate our method on XRA images and achieve the value of 85.00±3.66% for vessel segmentation, indicating that our method outperforms the other state-of-the-art methods.