SUnet: A multi-organ segmentation network based on multiple attention

doi:10.1016/j.compbiomed.2023.107596

SUnet: A multi-organ segmentation network based on multiple attention

2023-12-01

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DOI：10.1016/j.compbiomed.2023.107596

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Organ segmentation in abdominal or thoracic computed tomography (CT) images plays a crucial role in medical diagnosis as it enables doctors to locate and evaluate organ abnormalities quickly, thereby guiding surgical planning, and aiding treatment decision-making. This paper proposes a novel and efficient medical image seg-mentation method called SUnet for multi-organ segmentation in the abdomen and thorax. SUnet is a fully attention-based neural network. Firstly, an efficient spatial reduction attention (ESRA) module is introduced not only to extract image features better, but also to reduce overall model parameters, and to alleviate overfitting. Secondly, SUnet's multiple attention-based feature fusion module enables effective cross-scale feature integra-tion. Additionally, an enhanced attention gate (EAG) module is considered by using grouped convolution and residual connections, providing richer semantic features. We evaluate the performance of the proposed model on synapse multiple organ segmentation dataset and automated cardiac diagnostic challenge dataset. SUnet ach-ieves an average Dice of 84.29% and 92.25% on these two datasets, respectively, outperforming other models of similar complexity and size, and achieving state-of-the-art results.

Keywords:

Medical image segmentation

Transformer

Attention mechanism

Network architecture

Computed tomography

SUnet: A multi-organ segmentation network based on multiple attention

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Organization