An improved V-Net lung nodule segmentation model based on pixel threshold separation and attention mechanism.

ABSTRACT: Accurate labeling of lung nodules in computed tomography (CT) images is crucial in early lung cancer diagnosis and before nodule resection surgery. However, the irregular shape of lung nodules in CT images and the complex lung environment make it much more challenging to segment lung nodules accurately. On this basis, we propose an improved V-Net segmentation method based on pixel threshold separation and attention mechanism for lung nodules. This method first offers a data augment strategy to solve the problem of insufficient samples in 3D medical datasets. In addition, we integrate the feature extraction module based on pixel threshold separation into the model to enhance the feature extraction ability under different thresholds on the one hand. On the other hand, the model introduces ch

SUBMITTER: Ma X 

PROVIDER: S-EPMC10899216 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature