DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation

Automatic medical image segmentation has made great progress owing to powerful deep representation learning. Inspired by the success of self-attention mechanism in transformer, considerable efforts are devoted to designing the robust variants of the encoder-decoder architecture with transformer. How...

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Veröffentlicht in:IEEE transactions on instrumentation and measurement Jg. 71; S. 1 - 15
Hauptverfasser: Lin, Ailiang, Chen, Bingzhi, Xu, Jiayu, Zhang, Zheng, Lu, Guangming, Zhang, David
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Coders
Computer architecture
Data integration
Decoding
Feature extraction
Hierarchical swin transformer
Image enhancement
Image quality
Image segmentation
long-range contextual information
Medical diagnostic imaging
medical image segmentation
Medical imaging
Representations
Semantic segmentation
Semantics
Task analysis
transformer interactive fusion~(TIF) module
Transformers
ISSN:0018-9456, 1557-9662
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Zusammenfassung:Automatic medical image segmentation has made great progress owing to powerful deep representation learning. Inspired by the success of self-attention mechanism in transformer, considerable efforts are devoted to designing the robust variants of the encoder-decoder architecture with transformer. However, the patch division used in the existing transformer-based models usually ignores the pixel-level intrinsic structural features inside each patch. In this article, we propose a novel deep medical image segmentation framework called dual swin transformer U-Net (DS-TransUNet), which aims to incorporate the hierarchical swin transformer into both the encoder and the decoder of the standard U-shaped architecture. Our DS-TransUNet benefits from the self-attention computation in swin transformer and the designed dual-scale encoding, which can effectively model the non-local dependencies and multiscale contexts for enhancing the semantic segmentation quality of varying medical images. Unlike many prior transformer-based solutions, the proposed DS-TransUNet adopts a well-established dual-scale encoding mechanism that uses dual-scale encoders based on swin transformer to extract the coarse and fine-grained feature representations of different semantic scales. Meanwhile, a well-designed transformer interactive fusion (TIF) module is proposed to effectively perform multiscale information fusion through the self-attention mechanism. Furthermore, we introduce the swin transformer block into the decoder to further explore the long-range contextual information during the up-sampling process. Extensive experiments across four typical tasks for medical image segmentation demonstrate the effectiveness of DS-TransUNet, and our approach significantly outperforms the state-of-the-art methods.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2022.3178991