Focus segmentation method fusing PET (positron emission tomography) and CT (computed tomography) bimodal images

By integrating the lesion segmentation method of PET and CT dual-modal images, utilizing the dual-modal medical image segmentation model and interactive click signals, the problem of inaccurate lesion area segmentation in the existing technology is solved, and higher-precision lesion image segmentation is achieved.

CN120672776AActive Publication Date: 2025-09-19NORTHWEST UNIVERSITY FOR NATIONALITIES
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Patent Information

Application Number
CN202510777010.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-11
Publication Date
2025-09-19
Estimated Expiration
2045-06-11

AI Technical Summary

Technical Problem

Existing technologies have difficulty in accurately distinguishing lesion areas, especially when the grayscale difference between the tumor and surrounding tissue is small, resulting in segmentation boundary errors.

Method used

This lesion segmentation method uses a dual-modality fusion of PET and CT images. Lesion image data is acquired, separated into CT and PET data, and then fed into a dual-modality medical image segmentation model to generate a preliminary segmentation result. Based on this preliminary segmentation result, differential regions are generated, and differential region detection is performed to generate click signal data. The dual-modality medical image segmentation model is trained a preset number of times using the click signal data, CT data, and PET data to obtain the final lesion image segmentation result.

Benefits of technology

The accuracy of lesion image segmentation is improved. By combining multimodal feature complementarity and interactive click signals, dynamic learning and correction of segmentation intention are carried out, which significantly improves the accuracy of segmentation results.

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Abstract

The invention discloses a focus segmentation method fusing PET and CT bimodal images, and relates to the technical field of computers. The method comprises the following steps: acquiring focus image data, separating the focus image data to obtain CT data and PET data, and inputting the CT data and the PET data into a bimodal medical image segmentation model to obtain a preliminary segmentation result; a difference region is generated based on the preliminary segmentation result, difference region detection is carried out on the difference region, and click signal data is generated; the difference region is a region which is not marked as a focus in the preliminary segmentation result; training the bimodal medical image segmentation model for a preset number of times through the click signal data, the CT data and the PET data to obtain a segmentation result; and determining a segmentation result generated by the last training as a lesion image segmentation result. The method can improve the medical image lesion segmentation precision.
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