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Centerline-supervision Multi-task Learning Network for Coronary Angiography Segmentation by Yuanxiu Zhang; Yufeng Gao; Guangquan Zhou; Jianan He; Jun Xia; Guoyi Peng; Xiaojian Lou; Shoujun Zhou; Hui Tang; Yang Chen is a Medicine article available to read on EtoBox.

What is Centerline-supervision Multi-task Learning Network for Coronary Angiography Segmentation about?

With convolutional neural networks’ remarkable performance in computer vision, more and more studies are applying deep learning to vessel image segmentation tasks. This work focuses on the task of coronary X-ray angiography segmentation, which is critical in the diagnosis of cardiovascular disorders. For vascular segmentation in coronary X-ray angiography images, we propose a novel deep learning model based on the UNet backbone. We first equip a channel attention module in skip-connections to improve pixel-wise segmentation accuracy by emphasizing the effective channel in low-level features. A centerline auxiliary supervision module is also introduced at the network’s end to provide prior knowledge of vessel connectivity and thick vessels, utilizing the existing binary segmentation annotations efficaciously. Consequently, the network can devote more attention to pixels that ensure vessel tree connectivity and high confidence. Extensive experiments demonstrate the effectiveness of the two modules in improving the performance of the model. We compared our results to the recently proposed networks and revealed that these two modules can be added to other U-shaped networks to enhance p

Who reads Centerline-supervision Multi-task Learning Network for Coronary Angiography Segmentation?

Author

Yuanxiu Zhang; Yufeng Gao; Guangquan Zhou; Jianan He; Jun Xia; Guoyi Peng; Xiaojian Lou; Shoujun Zhou; Hui Tang; Yang Chen

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Physical Sciences)