Pulmonary embolism lesion segmentation method and system based on image recognition and storage medium

By employing vessel diameter gradient analysis, Weibull mixture model, and variational Bayesian inference techniques, this study addresses the problem of existing technologies being unable to accurately distinguish the morphological and hemodynamic characteristics of vessels at different levels. It enables high-precision detection of microemboli in subsegmental branches and quantification of embolism probability, ensuring the medical rationality of the segmentation results.

CN121330728BActive Publication Date: 2026-05-19ZHENGZHOU UNIV
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Patent Information

Application Number
CN202511477512.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-10-16
Publication Date
2026-05-19
Estimated Expiration
2045-10-16

AI Technical Summary

Technical Problem

Existing technologies cannot effectively distinguish the morphological differences and hemodynamic characteristics of blood vessels at different levels, resulting in low detection accuracy and high false positive rate of microembolism in subsegmental branches. They also lack accurate probability modeling and anatomical constraint verification, making it impossible to achieve accurate estimation of embolism probability and quantification of uncertainty.

Method used

Multi-level vascular topology data were extracted through vascular diameter gradient analysis. The density distribution within the blood vessels was modeled using a Weibull mixture model. The probability of embolism was estimated by combining variational Bayesian inference. Multi-scale feature fusion and vascular connectivity constraint optimization were performed to ensure the medical rationality of the segmentation results.

Benefits of technology

It achieves precise hierarchical identification of the main pulmonary artery, segmental branches, and subsegmental branches, improves the detection accuracy of vessels of different sizes, quantifies the uncertainty of embolism probability, and ensures that the segmentation results conform to vascular anatomy and hemodynamics.

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Abstract

The application relates to the technical field of image processing, and discloses a pulmonary embolism lesion segmentation method and system based on image recognition and a storage medium. The method comprises the following steps: extracting a multi-level blood vessel topological structure of a CTPA image through blood vessel diameter gradient analysis; modeling blood vessel density distribution by using a Weibull mixed model to obtain an embolism characteristic parameter; estimating a pixel embolism probability by using variational Bayesian inference to generate a lesion distribution map; performing multi-scale feature fusion on the lesion probability map to obtain a segmentation boundary; and optimizing the boundary based on blood vessel connectivity constraints to obtain a final embolism lesion segmentation result. The application solves the problems that blood vessel level differentiation processing cannot be realized, accurate probability modeling is lacked, and anatomic constraint verification is lacked. The application improves the precision of pulmonary embolism lesion segmentation.
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