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Le Zou, Xiangxu Bu, Fengling Jiang

IEEE Journal of Biomedical and Health Informatics

|June 25, 2025

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A novel lightweight decoder, LDMSD, enhances medical image segmentation by integrating deformable convolution and multi-scale self-attention. It significantly reduces computational load while improving accuracy, outperforming existing methods.

Area of Science:

Computer Vision
Medical Imaging
Artificial Intelligence

Background:

Medical image segmentation faces challenges with computationally intensive decoders, especially in resource-limited settings.
Existing decoder designs often limit segmentation performance due to their simplicity.
Balancing computational efficiency, lightweight design, and high accuracy in medical image segmentation is a significant challenge.

Purpose of the Study:

To introduce a novel, computationally efficient, and lightweight decoder for medical image segmentation.
To enhance the representational capacity and feature map augmentation in medical image segmentation models.
To improve the accuracy and computational efficiency of medical image segmentation.

Main Methods:

Developed a novel decoder integrating line deformable convolution and multi-scale self-attention (LDMSD).

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Incorporated a multi-scale self-attention enhancement module with two distinct mechanisms.

Utilized a linear deformable convolution attention-guided mechanism for skip connection feature augmentation.

Main Results:

LDMSD significantly improved representational capacity and feature map augmentation.
The method effectively captured global and multi-scale target information, accurately locating boundaries and structures.
LDMSD achieved a 77.36% reduction in FLOPs and an 81.66% reduction in parameters compared to CASCADE.

Conclusions:

LDMSD offers a superior balance between accuracy and computational efficiency for medical image segmentation.
The proposed decoder effectively addresses the limitations of conventional convolutions and enhances semantic relationship capture.
Experimental validation on six datasets confirms LDMSD's state-of-the-art performance in medical image segmentation.