ArticleScientific reports2024
An improved V-Net lung nodule segmentation model based on pixel threshold separation and attention mechanism.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- CA-3DTransUNet with dynamic cross-scale fusion for pulmonary nodule segmentation.Scientific reports · 2026Article
- Impact of CT Intensity and Contrast Variability on Deep-Learning-Based Lung-Nodule Detection: A Systematic Review of Preprocessing and Harmonization Strategies (2020-2025).Diagnostics (Basel, Switzerland) · 2026Review
- HSV-Net: hybrid state-space and self-supervised vision fusion with progressive contour learning for CT pulmonary nodule segmentation.Frontiers in oncology · 2026Article
- Article
- Deep learning-assisted 3D CT angiography for supply-vessel localization in inflammatory peripheral pulmonary artery pseudoaneurysms.Frontiers in medicine · 2026Article
- GLANCE: continuous global-local exchange with consensus fusion for robust nodule segmentation.NPJ digital medicine · 2025Article
- YOLOv8-BCD: a real-time deep learning framework for pulmonary nodule detection in computed tomography imaging.Quantitative imaging in medicine and surgery · 2025Article
- Improved lung nodule segmentation with a squeeze excitation dilated attention based residual UNet.Scientific reports · 2025Article
- An integrated method for detecting lung cancer via CT scanning via optimization, deep learning, and IoT data transmission.Frontiers in oncology · 2024Article
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4 authors.
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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 channel and spatial attention modules to make the model pay more attention to important semantic information and improve its generalization ability and accuracy. Experiments show that the Dice similarity coefficients of the improved model on the public datasets LUNA16 and LNDb are 94.9% and 81.1% respectively, and the sensitivities reach 92.7% and 76.9% respectively. which is superior to most existing UNet architecture models and comparable to the manual level segmentation results by medical technologists.
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