Abdominal multi-organ segmentation in Multi-sequence MRIs based on visual attention guided network and knowledge distillation.

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.