Segmented intelligent recognition model and recognition method for liver CT images

Through the cascaded liver segmentation and segmentation model, combined with the improved 3D-UNet network and residual module, the problems of insufficient intelligence of the liver surgical navigation system and rough liver segmentation results are solved, and more accurate liver segmentation recognition is achieved.

CN114937147BActive Publication Date: 2025-08-29ZHEJIANG UNIV
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Patent Information

Application Number
CN202210573370.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-05-25
Publication Date
2025-08-29
Estimated Expiration
2042-05-25

AI Technical Summary

Technical Problem

The existing liver surgical navigation system lacks intelligence and repeatability. The results of liver lesions are marked dependent on doctors' experience, and the existing deep learning results are rough when the liver is segmented, which affects the formulation of surgical plans.

Method used

The cascaded liver segmentation model and liver segmentation model are used, combined with the improved 3D-UNet network and residual module, and the attention residual module is used to segment liver CT images, first perform liver parenchymal area segmentation, and then perform couinaud segmentation.

Benefits of technology

It improves the accuracy and recognition ability of liver segmentation, achieves more accurate liver segmentation results, and improves the recognition ability of the model.

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Abstract

The present invention discloses a method for intelligent segmentation and recognition of liver CT images, comprising the following steps: S1. Using a liver segmentation model to segment the liver parenchyma region of an abdominal CT image to obtain a liver mask; S2. Multiplying the liver mask with the original input abdominal CT image to obtain a liver localization image; S3. Inputting the liver localization image into the liver segmentation model, which then performs Couinaud segmentation on the liver region. The present invention also discloses an intelligent segmentation and recognition model for liver CT images. The present invention can achieve more accurate liver segmentation recognition results.
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