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LMU-Net: lightweight U-shaped network for medical image segmentation

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A Correction to this article was published on 19 September 2023

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Abstract

Deep learning technology has been employed for precise medical image segmentation in recent years. However, due to the limited available datasets and real-time processing requirement, the inherently complicated structure of deep learning models restricts their application in the field of medical image processing. In this work, we present a novel lightweight LMU-Net network with improved accuracy for medical image segmentation. The multilayer perceptron (MLP) and depth-wise separable convolutions are adopted in both encoder and decoder of the LMU-Net to reduce feature loss and the number of training parameters. In addition, a lightweight channel attention mechanism and convolution operation with a larger kernel are introduced in the proposed architecture to further improve the segmentation performance. Furthermore, we employ batch normalization (BN) and group normalization (GN) interchangeably in our module to minimize the estimation shift in the network. Finally, the proposed network is evaluated and compared to other architectures on publicly accessible ISIC and BUSI datasets by carrying out robust experiments with sufficient ablation considerations. The experimental results show that the proposed LMU-Net can achieve a better overall performance than existing techniques by adopting fewer parameters.

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Acknowledgements

The authors would like to extend our sincere thanks to Jiangsu Citron Biotechnology Co., Ltd for all the support and help towards publishing this article.

Funding

This work is supported by Ahai Power Generation Branch of Yunnan Huadian Jinsha River Middle Water Power Development Co., Ltd and China Huadian Corporation (No. CHDKJ22-02-88).

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Authors and Affiliations

  1. Southwest Petroleum University, Chengdu, China

    Ting Ma & Ke Wang

  2. Jiangsu Citron Biotech Company Limited, Nantong, China

    Feng Hu

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  1. Ting Ma
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  2. Ke Wang
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  3. Feng Hu
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Correspondence to Feng Hu.

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The authors declare that they have no conflict of interest. This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

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Ma, T., Wang, K. & Hu, F. LMU-Net: lightweight U-shaped network for medical image segmentation. Med Biol Eng Comput 62, 61–70 (2024). https://doi.org/10.1007/s11517-023-02908-w

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