Deeply learned bronchial structures driven automatic bronchopulmonary segments segmentation

The three-dimensional reconstruction of bronchopulmonary segments based on computed tomography (CT) is very critical in lesion, lung cancer localization and surgical resection. However, there is currently no fast and accurate method for three-dimensional reconstruction of pulmonary segments, and the process of labeling pulmonary segments needs to rely on other information such as bronchi and blood vessels, which will greatly consume the time and mental cost of doctors. In this paper, based on the principle of pulmonary segments division, we propose a two-stage fast pulmonary segments division method based on segmental bronchi. Specifically, for a CT image, we employ two well-trained nnUNet models in the first stage to accurately segment 5 lobes and 18 segmental bronchi, respectively. This is because each pulmonary segment should encompass its corresponding segmental bronchi, while lung lobe boundaries exhibit greater distinctiveness compared to those of pulmonary segments. In the second stage, we consider the distance from each pixel point to the segmental bronchi of various pulmonary segments in each lobe, and further divide each lobe to obtain the final 18 types of segments. Finally, we visually validated the rationality of the results by employing the principle of using pulmonary veins as demarcations for pulmonary segments.