Zero sample cross-domain diffusion segmentation method based on anatomical structure probability transmission guidance

By constructing a dual-stream collaborative learning framework and an anatomical structure probability transfer-guided method, the performance degradation problem of cross-domain medical image segmentation models across different devices and modalities was solved, achieving zero-shot cross-domain segmentation, improving segmentation accuracy and robustness, and adapting to the needs of different clinical scenarios.

CN121121104AActive Publication Date: 2025-12-12BEIJING ZHENXINGDA TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202511207035.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-27
Publication Date
2025-12-12
Estimated Expiration
2045-08-27

AI Technical Summary

Technical Problem

Traditional medical image segmentation models suffer from a sharp decline in performance in cross-domain scenarios, and existing methods cannot achieve true zero-shot generalization, especially in applications across different devices and modalities.

Method used

We employ a zero-shot cross-domain diffusion segmentation method guided by anatomical structure probabilistic transfer. By constructing a two-stream collaborative learning framework, we utilize a shared structure encoder, anatomical decoder, and image appearance decoder, combined with anatomical loss and structural loss for training, to achieve zero-shot inference and hierarchical probabilistic structure transfer, avoiding reliance on target domain data.

Benefits of technology

It achieves zero-sample cross-domain segmentation, reduces data acquisition and model iteration costs, improves segmentation accuracy and structural fidelity, enhances robustness and interpretability, and adapts to the needs of different clinical scenarios.

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Abstract

The invention relates to the technical field of medical image processing, and discloses a zero-sample cross-domain diffusion segmentation method based on anatomical structure probability transmission guidance, and the method comprises the steps: constructing a double-flow collaborative learning framework, extracting multi-scale features through a shared structure encoder, learning unconditional anatomical prior through an anatomical decoder, and carrying out the segmentation of the non-conditional anatomical prior. And the image appearance decoder strengthens mode-independent structure perception and realizes appearance and structure decoupling. In the inference stage, a hierarchical probability structure transmission (H-PST) mechanism is introduced, multi-scale features are extracted from an unknown target domain image and a mask in the generation process, the structure distribution difference is measured by using a slice Wasserstein distance, reverse diffusion is dynamically guided by using gradient as a structure transmission force, and general anatomical prior is refined into a segmentation mask aligned with the target image. According to the method, cross-modal / equipment zero sample segmentation can be realized without target domain data, the accuracy, the structural fidelity, the robustness and the interpretability are improved, and the clinical deployment cost is reduced.
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