Deep learning-based bronchial tree-guided semi-automatic segmentation of pulmonary segments in computed tomography images.

ABSTRACT:

Background

Pulmonary segments are valuable because they can provide more precise localization and intricate details of lung cancer than lung lobes. With advances in precision therapy, there is an increasing demand for the identification and visualization of pulmonary segments in computed tomography (CT) images to aid in the precise treatment of lung cancer. This study aimed to integrate multiple deep-learning models to accurately segment pulmonary segments in CT images using a bronchial tree (BT)-based approach.

Methods

The proposed segmentation method for pulmonary segments using the BT-based approach comprised the following five essential steps: (I) segmentation of the lung using a U-Net (R231) (public access) model; (II) segmentation of the lobes using a V-Net (self-devel

SUBMITTER: Chen Z 

PROVIDER: S-EPMC10895116 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature