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STA-Former: enhancing medical image segmentation with Shrinkage Triplet Attention in a hybrid CNN-Transformer model

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Abstract

Convolutional neural networks (CNNs) have found extensive use in medical image segmentation tasks. However, they encounter limitations in capturing long-range semantic interactions. Conversely, Transformers excel at handling long-range dependencies but struggle to preserve local semantic details. To address this challenge, we propose STA-Former, a hybrid CNN-Transformer model for medical image segmentation. Our approach is founded on three fundamental principles: (1) We propose the Shrinkage Triplet Attention (STA) module to enhance feature fusion within the decoder. It focuses on spatial and channel interactions in the feature map, computes thresholds across dimensions, and suppresses irrelevant information through soft-thresholding. (2) We present a redesigned hierarchical hybrid CNN-Transformer encoder that connects CNN and Transformer blocks at multiple scales, enabling the capture of both long-range and short-range dependencies across various scales of feature maps. (3) Unlike traditional decoders that apply the attention mechanism exclusively to low-level features, our approach utilizes a multiscale attention hierarchical decoder, leveraging feature map correlations at different scales for effective feature fusion. Our method exhibits superior performance compared to the state-of-the-art methods on three datasets: Synapse multiorgan CT, ACDC cardiac MRI scans, and breast ultrasound image.

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Data availability

The authors confirm that the data supporting the findings of this study are available within the article and openly available in [Synapse], [ACDC], and [BUSI].

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Authors and Affiliations

  1. Manufacturing Electronics Research and Development Center, Institute of Microelectronics of the Chinese Academy of Sciences, No.3 Beitucheng West Road, Beijing, 100029, China

    Yuzhao Liu, Liming Han, Bin Yao & Qing Li

  2. School of Integrated Circuits, University of Chinese Academy of Sciences, Zhongguancun South Road, Beijing, 100020, China

    Yuzhao Liu, Liming Han & Qing Li

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  1. Yuzhao Liu
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  2. Liming Han
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  4. Qing Li
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Yuzhao Liu wrote the main manuscript text. Liming Han, Bin Yao, and Qing Li provide important suggestions for the manuscript. All authors reviewed the manuscript.

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Correspondence to Qing Li.

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Liu, Y., Han, L., Yao, B. et al. STA-Former: enhancing medical image segmentation with Shrinkage Triplet Attention in a hybrid CNN-Transformer model. SIViP 18, 1901–1910 (2024). https://doi.org/10.1007/s11760-023-02893-5

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