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DS-UNeXt: depthwise separable convolution network with large convolutional kernel for medical image segmentation

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Abstract

Accurate automatic segmentation of medical images is required in computer-aided diagnosis systems in clinical medicine. Convolutional neural networks (CNNs) based on U-shaped structures are widely used in medical image segmentation tasks. However, due to the intrinsic locality of the convolution operation, it is difficult for CNN-based approaches to learn the global information and long-range semantic information interactions using Swin-Unet. However, we find that UNet and Swin-Unet have the worst segmentation performance on small masses. To remedy this problem, this paper presents an end-to-end depthwise separable U-shaped convolution network with a large convolution kernel (DS-UNeXt) for the medical image segmentation of computed tomography (CT) images and magnetic resonance images (MRIs). Our network has a larger receptive field to extract features, which is useful for boosting the performance of multiscale medical segmentations. In DS-UNeXt, parallel depthwise separable spatial pooling (PDSP) is proposed to aggregate the global information. PDSP consists of multiple parallel depthwise separable convolutions to enhance the high-level semantic features. The proposed DS-UNeXt achieves Dice indices of 80.65% and 90.88% on the synapse for the multiorgan segmentation dataset and the automatic cardiac diagnosis challenge (ACDC) dataset, respectively. Moreover, extensive experiments show that DS-UNeXt transcends several state-of-the-art segmentation networks.

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Availability of data and materials

We conducted experiments on three datasets, including the Synapse for multiorgan CT segmentation dataset and ACDC dataset. The Synapse for multiorgan CT segmentation dataset can be found in https://www.synapse.org/#!Synapse:syn3193805/wiki/217789. The ACDC dataset can be found in https://acdc.creatis.insa-lyon.fr/description/databases.html.

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Funding

This work was supported by the Natural Science Foundation of Chongqing, China (Grant No. cstc2021jcyj-msxmX0605), and Science and Technology Foundation of Chongqing Education Commission (Grant No. KJQN202001137).

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Authors and Affiliations

  1. School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 40400, China

    Tongyuan Huang, Jiangxia Chen & Linfeng Jiang

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  1. Tongyuan Huang
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  2. Jiangxia Chen
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Contributions

T.H. and J.C. contributed to conceptualization, methodology, software, 402, validation, formal analysis, investigation, 403 resources, data curation, writing—original draft preparation, writing—review and editing, and visualization. L.J. contributed to supervision, project 405 administration, and funding acquisition. All authors have read and agreed to the published 406 version of the manuscript.

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Correspondence to Tongyuan Huang.

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Ethical approval

Synapse for multiorgan CT segmentation dataset and ACDC dataset belongs to public datasets. The patients involved in the dataset have obtained ethical approval. User can download relevant data for free for research and publish relevant articles. Our study is based on open-source data, so there are no ethical issues.

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Huang, T., Chen, J. & Jiang, L. DS-UNeXt: depthwise separable convolution network with large convolutional kernel for medical image segmentation. SIViP 17, 1775–1783 (2023). https://doi.org/10.1007/s11760-022-02388-9

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