ResUnet medical image segmentation system based on edge operator improvement
By designing a new loss function in the ResUnet medical imaging segmentation system and using the labeled data processed by the edge operator, the problem that the existing system fails to effectively utilize anatomical structure knowledge and image edge features is solved, achieving higher segmentation accuracy and faster training speed.
CN114419083BActive Publication Date: 2025-03-21SHAN DONG MSUN HEALTH TECH GRP CO LTD
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Patent Information
- Application Number
- CN202111542921.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-12-16
- Publication Date
- 2025-03-21
- Estimated Expiration
- 2041-12-16
AI Technical Summary
Technical Problem
The existing deep learning-based medical imaging segmentation system fails to effectively utilize medical-related anatomical structure knowledge and edge feature information of image data, resulting in poor segmentation effect of structural edges.
Method used
The ResUnet medical image segmentation system based on edge operators is adopted to design a new loss function during the training process, and the intelligent feature learning of the model is optimized through the weighted sum of the prediction results and the annotation standard data and the annotation data processed by the Canny operator.
Benefits of technology
It significantly improves the accuracy of organ structure segmentation, reduces model training time, and reduces the need for labeled data volume.
✦ Generated by Eureka AI based on patent content.
Abstract
The present invention provides an improved ResUnet medical image segmentation system based on an edge operator, comprising: a data acquisition module, configured to: acquire medical image data to be processed; a lung segmentation module, configured to: obtain lung segmentation results according to the acquired medical image data and a preset ResUnet network model; wherein a first loss function is obtained according to a prediction result and labeled standard data, a second loss function is obtained according to the prediction result and the labeled data processed by the edge operator, and a weighted sum of the first loss function and the second loss function is used as the loss function of the ResUnet network model; the present invention performs optimization adjustment during the training process and redesigns the loss function of model training and learning, which can perform intelligent feature learning more quickly and greatly improve the accuracy of organ structure segmentation.
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