Abstract
As one of the key problems in computer-aided medical image analysis, learning how to model global relationships and extract local details is crucial to improve the performance of abdominal multi-organ segmentation. While current techniques for Convolutional Neural Networks (CNNs) are quite mature, their limited receptive field makes it difficult to balance the ability to capture global relationships with local details, especially when stacked onto deeper networks. Thus, several recent works have proposed Vision Transformer based on a self-attentive mechanism and used it for abdominal multi-organ segmentation. However, Vision Transformer is computationally expensive by modeling long-range relationships on pairs of patches. To address these issues, we propose a novel multi-organ segmentation framework, named GDTNet, based on the synergy of CNN and Transformer for mining global relationships and local details. To achieve this goal, we innovatively design a Dilated Attention Module (DAM) that can efficiently capture global contextual features and construct global semantic information. Specifically, we employ a three-parallel branching structure to model the global semantic information of multiscale encoded features by Dilated Transformer, combined with global average pooling under the supervision of Gate Attention. In addition, we fuse each DAM with DAMs from all previous layers to further encode features between scales. Extensive experiments on the Synapse dataset show that our method outperforms ten other state-of-the-art segmentation methods, achieving accurate segmentation of multiple organs in the abdomen.
C. Zhang and Z. Wang—These authors contributed equally to this paper.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants 62272404 and 62372170, in part by the Open Project of Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province under Grant YXZN2022004, in part by the Natural Science Foundation of Hunan Province of China under Grants 2022JJ30571 and 2023JJ40638, in part by the Research Foundation of Education Department of Hunan Province under Grant 21B0172, in part by the Innovation and Entrepreneurship Training Program for China University Students under Grant 202210530002, and in part by the Baidu Program.
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Zhang, C., Wang, Z., Zhang, Y., Li, X., Hu, K. (2024). GDTNet: A Synergistic Dilated Transformer and CNN by Gate Attention for Abdominal Multi-organ Segmentation. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14557. Springer, Cham. https://doi.org/10.1007/978-3-031-53302-0_4
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