Method for detecting tree-shaped structure bifurcation key point in three-dimensional tomography image

Abstract

The invention provides a method for detecting tree-shaped structure bifurcation key points in a three-dimensional tomography image, and relates to the field of medical image processing. According to the method, a three-dimensional tomography image containing an anatomical tree-shaped structure is acquired and preprocessed in an offline stage, the image is labeled, a corresponding prediction targetof bifurcation key point detection, tree-shaped structure segmentation and a branch vector field is generated according to a result, finally training data is formed and used for training a deep learning network, and a trained network is obtained; in the online stage, an image of the same type is acquired, the prediction results of a bifurcated key point heat map, the tree structure segmentation probability map and the branch vector field corresponding to the image are output by using the trained network, and the final key point detection position is calculated by applying the prediction results of the heat map and the segmentation probability map. The method can be widely applied to detection of bifurcation key points such as tracheas, arteries and veins in various anatomical tree-shapedstructures, and a good detection effect can be achieved.

Description

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AI Technical Summary

Benefits of technology

This technology has several benefits that make it useful for people who want better ways to manage their health by providing them with more accurate data on how they're doing well or at least improving their overall quality level through regular checkups.

Problems solved by technology

Technicians use computerized tomography scanning techniques to analyze computed tomograms obtained by X ray fluorescence angiography (XF). However, current automated tools used during diagnostic procedures require manual input and interpretation, leading to errors when analyzing complex anatomic structures like the ascending thoracic arch. Additionally, there may exist issues associated with identifying critical regions within the CT scan data itself based solely upon their own visual perception. Current solutions involve manually annotating relevant areas around them through annotation labels, making it challenging even if they were accurately detected.

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