Two-stage full 3D abdominal organ segmentation method and system based on dual-resolution network

Through a two-stage full 3D abdominal organ segmentation method based on deep dual-resolution network, combined with random data augmentation and feature fusion technology, the problems of low accuracy and long training time in abdominal medical image segmentation are solved, and fast and accurate abdominal organ segmentation is achieved.

CN114998307BActive Publication Date: 2025-03-18CHONGQING UNIV
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Patent Information

Application Number
CN202210796459.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-07-06
Publication Date
2025-03-18
Estimated Expiration
2042-07-06

AI Technical Summary

Technical Problem

The existing medical image segmentation technology has problems such as low accuracy, long training time, large parameters, overfitting and loss of detailed information in abdominal organ segmentation, especially in small organs and cases with large deformation.

Method used

A two-stage full 3D abdominal organ segmentation method based on deep dual-resolution network is adopted. Through the combined use of a coarse segmentation network and a fine segmentation network, combined with random data enhancement, dual-resolution branch feature fusion and anisotropic pyramid pooling module, the generalization ability and segmentation accuracy of the model are improved.

Benefits of technology

It realizes fast and accurate segmentation of all 3D abdominal organs, shortens the segmentation task cycle, improves the robustness and segmentation accuracy of the model, and reduces the training time and parameter quantity.

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Abstract

The present invention belongs to the technical field of medical image segmentation, and specifically discloses a two-stage full 3D abdominal organ segmentation method and system based on a dual-resolution network. The method preprocesses a data set and an original image, performs random data enhancement on the preprocessed data set and the original image, trains a coarse segmentation network and a fine segmentation network according to the data set, scales the original image after data enhancement to a preset size and then inputs it into the coarse segmentation network to perform abdominal organ segmentation, obtains an ROI image according to the ROI area obtained by the coarse segmentation and scales it to a preset size, and then inputs it into the fine segmentation network to obtain a segmentation result. The present technical solution uses a two-stage method to achieve the segmentation of full 3D abdominal organs.
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