Auxiliary image segmentation method for bronchoscopy

Through significant feature analysis and physiological lens feature monitoring, the particle size of the bronchoscopic image segmentation is dynamically adjusted, and the problem of insufficient segmentation strategy in traditional methods is solved, achieving high-precision airway image segmentation to reduce false detection and missed detection.

CN120259344AActive Publication Date: 2025-07-04THE FIRST MEDICAL CENT CHINESE PLA GENERAL HOSPITAL
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Patent Information

Application Number
CN202510724694.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-03
Publication Date
2025-07-04
Estimated Expiration
2045-06-03

AI Technical Summary

Technical Problem

Traditional tracheal image segmentation methods cannot dynamically adjust the segmentation strategy according to image characteristics, making it difficult to adapt to the needs of different lesion areas, which can easily lead to missed detection or missed detection.

Method used

Through significant feature analysis, determine the significant area of ​​the airway, configure the initial image segmentation particle size scheme, synchronously monitor the user's physiological characteristics and lens operation characteristics, generate the image segmentation compensation coefficient, and adjust the initial segmentation particle size scheme for image segmentation.

Benefits of technology

Significantly improve the segmentation accuracy and accuracy of airway images, reduce false detection and missed detection, and achieve efficient and accurate image segmentation.

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Abstract

The invention relates to an auxiliary image segmentation method for bronchoscopy, and relates to the field of medical image processing, and the method comprises the steps: carrying out the significant feature analysis of an airway image, and determining a plurality of airway significant regions; configuring an initial image segmentation granularity scheme according to the plurality of image saliency degrees; synchronously monitoring and acquiring user physiological features and lens operation features, performing image fuzziness prediction, and generating an image segmentation compensation coefficient; and adjusting the initial image segmentation granularity scheme to obtain a corrected image segmentation granularity scheme, and performing image segmentation on the plurality of airway salient regions. According to the method, the problems that the requirements of different lesion areas are difficult to adapt and false detection or missing detection is easily caused due to the fact that a segmentation strategy cannot be dynamically adjusted according to image features in a traditional method can be solved; according to the method, the segmentation precision and accuracy of the trachea image can be remarkably improved, and efficient and accurate image segmentation is realized, so that different types of lesion areas can be better identified, and false detection and missing detection are effectively reduced.
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