Medical image segmentation method and system based on residual Mama and multi-scale boundary enhancement

By employing residual Mamba and multi-scale boundary enhancement in medical image segmentation, this method addresses the challenges of balancing long-range semantic dependency modeling with computational efficiency, lack of adaptive fusion of multi-scale lesion features, and insufficient segmentation of regions with blurred boundaries in existing technologies. It achieves high-precision and stable medical image segmentation, applicable to complex backgrounds and diverse texture scenes.

CN121544644APending Publication Date: 2026-02-17NINGBO MEDICAL CENT LIHUILI HOSPITACL
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Patent Information

Application Number
CN202610075505.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-20
Publication Date
2026-02-17

AI Technical Summary

Technical Problem

Existing medical image segmentation technologies struggle to balance long-range semantic dependency modeling and computational efficiency, lack adaptive fusion mechanisms for multi-scale lesion features, and have insufficient ability to segment regions with blurred boundaries, resulting in limitations in the completeness, accuracy, and clinical applicability of segmentation results.

Method used

A medical image segmentation method based on residual Mamba and multi-scale boundary enhancement is adopted. Through an encoder-decoder architecture, residual convolutional blocks, residual Mamba blocks, multi-scale gated attention modules and boundary enhancement modules are combined to achieve long-range spatial dependence and multi-scale feature fusion and optimize boundary representation.

Benefits of technology

It significantly improves segmentation accuracy, boundary quality, and multi-scale adaptability, balancing computational efficiency with clinical applicability. It is suitable for complex backgrounds and diverse texture scenes, improving the accuracy and stability of medical image segmentation.

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Abstract

The invention relates to a medical image segmentation method and system based on residual Mama and multi-scale boundary enhancement. The method comprises the following steps: acquiring and preprocessing a medical image; inputting the image into a segmentation model based on an encoder-decoder architecture; the encoder synchronously extracts local texture features and models long-range spatial dependence through residual error convolution blocks and residual error Mama blocks which are alternately connected; fusing and enhancing the jump connection features between the encoder and the decoder through a boundary enhancement module to optimize boundary characterization; integrating a multi-scale gating attention module in a decoding path, and adaptively selecting and fusing multi-scale context features; and finally outputting the high-precision segmentation mask. The method effectively solves the problems that in the prior art, long-range dependence and local details are difficult to consider, the multi-scale feature fusion capability is insufficient, boundary segmentation is fuzzy and the like, and the segmentation accuracy, the boundary continuity and the clinical practicability are remarkably improved.
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