Automatic segmentation of pulmonary lobes on low-dose computed tomography using deep learning.

ABSTRACT:

Background

To develop and validate a fully automated deep learning-based segmentation algorithm to segment pulmonary lobe on low-dose computed tomography (LDCT) images.

Methods

This study presents an automatic segmentation of pulmonary lobes using a fully convolutional neural network named dense V-network (DenseVNet) on lung cancer screening LDCT images. A total of 160 LDCT cases for lung cancer screening (100 in the training set, 10 in the validation set, and 50 in the test set) was included in this study. Specifically, the template of pulmonary lobes (the right lung consists of three lobes, and the left lung consists of two lobes) were obtained from pixel-level annotations by semiautomatic segmentation platform. Then, the model was trained under the supervision of the LDCT

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC7944332 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature