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. 2024 Feb 28;10(5):e26775.
doi: 10.1016/j.heliyon.2024.e26775. eCollection 2024 Mar 15.

SCANeXt: Enhancing 3D medical image segmentation with dual attention network and depth-wise convolution

Yajun Liu  1 , Zenghui Zhang  1 , Jiang Yue  2 , Weiwei Guo  3
Affiliations

SCANeXt: Enhancing 3D medical image segmentation with dual attention network and depth-wise convolution

Yajun Liu et al. Heliyon. .

Abstract

Existing approaches to 3D medical image segmentation can be generally categorized into convolution-based or transformer-based methods. While convolutional neural networks (CNNs) demonstrate proficiency in extracting local features, they encounter challenges in capturing global representations. In contrast, the consecutive self-attention modules present in vision transformers excel at capturing long-range dependencies and achieving an expanded receptive field. In this paper, we propose a novel approach, termed SCANeXt, for 3D medical image segmentation. Our method combines the strengths of dual attention (Spatial and Channel Attention) and ConvNeXt to enhance representation learning for 3D medical images. In particular, we propose a novel self-attention mechanism crafted to encompass spatial and channel relationships throughout the entire feature dimension. To further extract multiscale features, we introduce a depth-wise convolution block inspired by ConvNeXt after the dual attention block. Extensive evaluations on three benchmark datasets, namely Synapse, BraTS, and ACDC, demonstrate the effectiveness of our proposed method in terms of accuracy. Our SCANeXt model achieves a state-of-the-art result with a Dice Similarity Score of 95.18% on the ACDC dataset, significantly outperforming current methods.

Keywords: 3D medical image segmentation; Depth-wise convolution; Dual attention; InceptionNeXt; Swin transformer.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
Overview of our SCANeXt structure.
Figure 2
Figure 2
Components of the spatial-wise transformer.
Figure 3
Figure 3
Components of the channel-wise transformer.
Figure 4
Figure 4
Components of the depthwise convolution module.
Figure 5
Figure 5
Qualitative comparison of the segmentation performance for the Synapse dataset.
Figure 6
Figure 6
Qualitative comparison of the segmentation performance for the BraTS dataset.
Figure 7
Figure 7
Qualitative comparison of the segmentation performance for the ACDC dataset.
Figure 8
Figure 8
The model size vs. DSC is shown in this plot. Circle size indicates computational complexity by FLOPs.
See this image and copyright information in PMC

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