DAT-SegNet: a medical multi-organ segmentation network based on deformation attention and transformer

Wenxuan Huang; Chunlin Liu; Ruiyi Li; Peizhi Chen

doi:10.1117/12.2684274

1 August 2023 DAT-SegNet: a medical multi-organ segmentation network based on deformation attention and transformer

Wenxuan Huang, Chunlin Liu, Ruiyi Li, Peizhi Chen

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127540K (2023) https://doi.org/10.1117/12.2684274
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

With the development of Transformer and its derivatives over the last two years, several research have integrated Transformer with CNN or in instead of CNN to advance medical image segmentation. Although they often create acceptable feature maps for large organs, segmentation accuracy for small organs is less than satisfactory. Transformer excels at global context, but it displays limits in capturing fine-grained features, especially in medical areas, because convolution is unable to describe long-term correlations prevalent in images. This is because local information modeling lacks a spatial inductive bias. Currently available Transformer-based segmentation networks are not often optimized for this issue, we therefore use transformer as the backbone and provide a medical image segmentation network with deformation attention. The model uses attention mechanism to boost the impact of feature maps to address the issue of low accuracy of tiny organ segmentation in multi-organ segmentation tasks. On the Synapse dataset, our model has so far produced SOTA results.

Citation Download Citation

Wenxuan Huang, Chunlin Liu, Ruiyi Li, and Peizhi Chen "DAT-SegNet: a medical multi-organ segmentation network based on deformation attention and transformer", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127540K (1 August 2023); https://doi.org/10.1117/12.2684274

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KEYWORDS

Image segmentation

Transformers

Medical imaging

Deformation

Education and training

Convolution

Feature extraction

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