A method for skin scar image segmentation

Abstract

This invention relates to the field of medical image processing, specifically to a skin scar image segmentation method for automated skin scar region identification and quantitative analysis. It addresses the problems of low segmentation accuracy, inaccurate boundary modeling, and unreliable quantization calculations caused by insufficient feature representation, weak generalization ability, and reliance on manual intervention in existing methods. The method includes: S1 acquiring a skin scar image; S2 inputting a trained improved SAM2 segmentation network; and S3 outputting a segmentation mask. The improved SAM2 network is trained by constructing and preprocessing a dataset; it includes a frozen-parameter backbone image encoder, a cue encoder, a medical image feature guidance module (extracting texture details, color contrast, and edge morphology features), a residual enhancement module (fusing general features, cue vectors, and medical features through residual connections), and a mask decoder. During training, the backbone network parameters are frozen, and only the cue encoder, medical feature guidance module, residual enhancement module, and mask decoder are fine-tuned.

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