Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2024
Abdominal multi-organ segmentation in Multi-sequence MRIs based on visual attention guided network and knowledge distillation.
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Fu Hao, Zhang Jian, Li Bin, Chen Lanlan, Zou Junzhong, Zhang ZhuiYang, Zou Hao
Аннотация
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.
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.