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Medical & Biological Engineering & Computing

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30-11-2023 | Original Article

SCAN: sequence-based context-aware association network for hepatic vessel segmentation

Authors: Yinghong Zhou, Yu Zheng, Yinfeng Tian, Youfang Bai, Nian Cai, Ping Wang

Published in: Medical & Biological Engineering & Computing | Issue 3/2024

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Abstract

Accurate segmentation of hepatic vessel is significant for the surgeons to design the preoperative planning of liver surgery. In this paper, a sequence-based context-aware association network (SCAN) is designed for hepatic vessel segmentation, in which three schemes are incorporated to simultaneously extract the 2D features of hepatic vessels and capture the correlations between adjacent CT slices. The two schemes of slice-level attention module and graph association module are designed to bridge feature gaps between the encoder and the decoder in the low- and high-dimensional spaces. The region-edge constrained loss is designed to well optimize the proposed SCAN, which integrates cross-entropy loss, dice loss, and edge-constrained loss. Experimental results indicate that the proposed SCAN is superior to several existing deep learning frameworks, in terms of 0.845 DSC, 0.856 precision, 0.866 sensitivity, and 0.861 F1-score.

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Title: SCAN: sequence-based context-aware association network for hepatic vessel segmentation
Authors: Yinghong Zhou
Yu Zheng
Yinfeng Tian
Youfang Bai
Nian Cai
Ping Wang
Publication date: 30-11-2023
Publisher: Springer Berlin Heidelberg
Published in: Medical & Biological Engineering & Computing / Issue 3/2024
Print ISSN: 0140-0118
Electronic ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-023-02975-z

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