Bronchial segmentation method of lung CT image, related system and storage medium

Abstract

The invention provides a bronchus segmentation method for a lung CT image. The method comprises the following steps: (a) acquiring the lung CT image and labeling a bronchus; (b) preprocessing the lung CT image, including lung parenchyma extraction and data normalization, and calculating a lung boundary distance map corresponding to the image; (c) inputting the preprocessed image, the voxel coordinates and the distance map to the lung boundary into a 3D Unet network model for end-to-end training to obtain a 3D Unet training model, and in the training process, adopting a Laplace filter to enhance the boundary region of the image and calculating boundary enhancement loss (LBE) and Dice loss; and (d) performing bronchial segmentation based on the obtained 3D Unet training model. According to the invention, the voxel coordinates of the CT image and the distance from the voxel coordinates to the lung boundary serve as additional semantic information to be input into the bronchial segmentation model, boundary enhancement loss is introduced in the model training process, the segmentation performance of the tracheal boundary area is improved to the maximum extent, and leakage and breakage of tracheal segmentation are reduced.

Description

technical field [0001] The invention relates to the processing of CT images of lungs, in particular to a method for segmenting bronchi of CT images of lungs, a related system, and a corresponding computer storage medium. Background technique [0002] Numerous lung diseases, including bronchiectasis, chronic obstructive pulmonary disease (COPD) and lung cancer, pose a huge threat to human health. Standard computed tomography (CT, computed tomography) imaging can help radiologists detect lesions. For tracheal and bronchial surgery, reconstruction of the tracheal tree model on CT scans is generally considered a prerequisite for disease diagnosis. Manual segmentation of bronchial airways is time-consuming and labor-intensive due to their tree-like structure and variability in size, shape, and strength. [0003] At present, some researchers have proposed several airway segmentation methods based on CT images. Van Rikxoort et al proposed a region growing method with an adaptive...

AI Technical Summary

Problems solved by technology

While such loss functions are able to handle the class imbalance problem that often occurs in medical image segmentation tasks, the boundary problem is not well resolved because these functions treat all pixels / voxels equally

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