文献类型：期刊文献

英文题名：Abdominal multi-organ segmentation in Multi-sequence MRIs based on visual attention guided network and knowledge distillation

作者：Fu, Hao[1];Zhang, Jian[1];Li, Bin[2];Chen, Lanlan[1];Zou, Junzhong[1];Zhang, ZhuiYang[2];Zou, Hao[3]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Dept Automat, Shanghai 200237, Peoples R China;[2]Jiangnan Univ, Wuxi 2 Peoples Hosp, Med Ctr, Wuxi 214000, Jiangsu, Peoples R China;[3]Tsinghua Univ Shenzhen, Res Inst, Ctr Intelligent Med Imaging & Hlth, Shenzhen 518000, Peoples R China

年份：2024

卷号：122

外文期刊名：PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001248125800002)】；

基金：The research was supported by the National Natural Science Foundation of China (Nos. 61976091 and 62376095) . And we would like to thank the Wuxi Taihu Lake Talent Plan, leading Talents in Medical and Health Profession.

语种：英文

外文关键词：Abdominal multi-organ segmentation; Unpaired multi-sequence learning; Knowledge distillation; VAG-net

摘要：Purpose: The segmentation of abdominal organs in magnetic resonance imaging (MRI) plays a pivotal role in various therapeutic applications. Nevertheless, the application of deep -learning methods to abdominal organ segmentation encounters numerous challenges, especially in addressing blurred boundaries and regions characterized by low -contrast. Methods: In this study, a multi -scale visual attention -guided network (VAG-Net) was proposed for abdominal multi -organ segmentation based on unpaired multi -sequence MRI. A new visual attention -guided (VAG) mechanism was designed to enhance the extraction of contextual information, particularly at the edge of organs. Furthermore, a new loss function inspired by knowledge distillation was introduced to minimize the semantic disparity between different MRI sequences. Results: The proposed method was evaluated on the CHAOS 2019 Challenge dataset and compared with six state-of-the-art methods. The results demonstrated that our model outperformed these methods, achieving DSC values of 91.83 +/- 0.24% and 94.09 +/- 0.66% for abdominal multi -organ segmentation in T1 -DUAL and T2-SPIR modality, respectively. Conclusion: The experimental results show that our proposed method has superior performance in abdominal multi -organ segmentation, especially in the case of small organs such as the kidneys.