Medical image segmentation method based on mamba network and imaging method

By constructing a medical image segmentation method based on Mamba networks and combining residual connections, convolution, and interactive attention schemes, the method addresses the issues of insufficient reliability and accuracy in existing medical image segmentation technologies. It achieves efficient modeling of complex structures and boundary details, thereby improving segmentation performance.

CN120689296BActive Publication Date: 2026-07-21CENT SOUTH UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CENT SOUTH UNIV
Filing Date
2025-06-11
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing medical image segmentation methods suffer from insufficient reliability and accuracy in medical image segmentation tasks. Furthermore, the existing Vision Mamba architecture is ill-suited to the complex and varied structural morphology and directional heterogeneity of medical images, lacks an effective feature preservation mechanism in deep state modeling, and struggles to achieve semantic coordination between spatially discontinuous regions.

Method used

A medical image segmentation method based on Mamba networks is adopted, which combines residual connection scheme, convolution scheme and interactive attention scheme to construct encoding module, graph interactive attention module and decoding module. Through multi-path and multi-directional scanning strategy and channel attention control mechanism, the stability and semantic consistency of feature representation are enhanced, and efficient modeling of complex structures and boundary details is achieved.

Benefits of technology

It improves the reliability and accuracy of medical image segmentation, enhances the model's ability to represent complex structures and boundary details, and improves segmentation results.

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Abstract

The application discloses a medical image segmentation method based on a Mamba network, comprising the following steps: acquiring existing medical image data information; pre-processing the acquired medical image data information to construct a training data set; constructing an initial model of medical image segmentation based on the Mamba network, a residual connection scheme, a convolution scheme and an interactive attention scheme, and training to obtain a medical image segmentation model based on the Mamba network; and using the obtained medical image segmentation model based on the Mamba network to perform actual medical image segmentation. The application further discloses an imaging method comprising the medical image segmentation method based on the Mamba network. The application not only realizes medical image segmentation, but also has higher reliability, better accuracy and better robustness.
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