Ms. Jingyu Liu Profile

Jingyu Liu

at Tianjin Univ

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Proceedings Article | 23 July 2022 Paper

An intravascular ultrasound segmentation network based on multi-task learning

Jingyu Liu, Wenyue Hao, Zhongwei Jia, Yi Wang, Xiaodong Chen, Huaiyu Cai

Proceedings Volume 12277, 122770D (2022) https://doi.org/10.1117/12.2616202

KEYWORDS: Intravascular ultrasound, Image segmentation, Edge detection, Computer programming, Blood vessels, Image processing algorithms and systems, Image fusion, Network architectures, Information fusion, 3D modeling

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Intravascular ultrasound(IVUS) technology is one of the main technologies used to diagnose atherosclerosis. The accurate segmentation of the lumen and media area in IVUS images can help doctors perform clinical evaluations well. To overcome the effects of severe ultrasound speckles, various artifacts, and lesions in IVUS images, and improve the accuracy of IVUS image segmentation, an IVUS segmentation network MFU-Net based on multi-task learning is proposed. The algorithm selects the UNet network as the basic structure and introduces edge detection as an auxiliary task to build a multi-branch fusion deep neural network, which can force the network to pay attention to the edge information. The MFU-Net performance was verified on the 20MHz IVUS images data set, which is constructed by clinically IVUS images including a large number of interfering structures, such as calcified lesions, side vessels, vascular bifurcation, and stents. The artificial labels were annotated by two researchers with the guidance of a professional cardiologist. The experiment results show that the MFU-Net achieves 0.86 Jaccard measure(JM) for the media area and 0.91 Jaccard measure(JM) for the lumen area. Compared with the single-task UNet structure, the MFU-Net has higher segmentation accuracy and robustness and has a significant improvement in IVUS images containing vascular bifurcation and calcification.

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