多尺度特征融合的轻量化Transformer医学图像分割研究

doi:10.3969/j.issn.0258-8021.2025.02.004

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Abstract UNet has been widely used in the field of medical image segmentation, and its U-shaped encoder-decoder structure has become one of the most popular frameworks. However, the classification and localization accuracy of UNet are limited by the local receptive field of convolutions, which restricts its ability to effectively capture long-range dependencies. Transformer has been demonstrated outstanding capabilities in capturing long-range dependencies and serves as the core supporting technology for current large language models, addressing the limitations of convolutional neural networks. In this paper, a novel medical image segmentation model referred to as MoFormer was proposed. Based on the encoding decoding structure of UNet, this model integrated Transformer learning mechanism in the encoder to expand its context aware field of view and enhanced the multi-scale feature extraction ability of local and global information. The proposed MoFormer with random initialization achieved an average Dice coefficient of 0.823 on the BTCV dataset with 50 abdominal CT images. On the ISIC2017 dataset containing 2 750 dermoscopy images, it performed equally well as TransFuse but with 10.91 M fewer parameters. On the polyp dataset which includes 2 590 endoscopic images, it outperformed other popular comparison models, such as PraNet, with the increase in mIoU value by an average of 0.123. Overall, this neural network model balances the number of parameters with segmentation accuracy, demonstrating strong generalization across various medical image datasets.

Key words： U-shaped network Transformer multi-scale feature lightweight medical images segmentation

Received: 22 August 2023

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R318

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	Articles by authors
	Wang Xiaowei
	Xing Shuli
	Mao Guojun

Cite this article:

Wang Xiaowei,Xing Shuli,Mao Guojun. Research on Lightweight Transformer Medical Image Segmentation with Multi-Scale FeatureFusion[J]. Chinese Journal of Biomedical Engineering, 2025, 44(2): 165-173.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2025.02.004 OR http://cjbme.csbme.org/EN/Y2025/V44/I2/165

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