Dual-channel grouped cross-dimension attention V-Net for pulmonary nodule segmentation.

ABSTRACT:

Background

Accurate segmentation of pulmonary nodules is crucial for the diagnosis and treatment of early-stage lung cancer as it can aid clinicians in formulating effective treatment plans, increasing the chance of early detection and treatment, and reducing mortality. However, pulmonary nodules are similar to surrounding tissues, and the location, size, and quantity of nodules in different patients are unpredictable, posing challenges to accurate segmentation. This study aimed to develop a new deep learning network based on V-Net to address the deficiencies in pulmonary nodule segmentation tasks.

Methods

This work proposes a dual-channel grouped cross-dimension attention V-Net (DGCA V-Net) model for computed tomography (CT) pulmonary nodule segmentation. In downsampling, t

SUBMITTER: Zhang L 

PROVIDER: S-EPMC12514721 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature