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A multi-stage 3D convolutional neural network algorithm for CT-based lung segment parcellation
Trishul Siddharthan1, Zhoubing Xu2, Bruce Spottiswoode2
1Division of Pulmonary, Critical Care and Sleep Medicine, University of Miami, Miami, Florida, USA.
Journal of Applied Clinical Medical Physics
|July 23, 2025
Summary
Deep learning accurately segments lung regions on CT scans, improving analysis for patients with airway diseases. This advanced lung parcellation technique shows promise for clinical applications.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Pulmonology
Background:
- Traditional lung parcellation relies on fissures for lobar volume estimation.
- Deep learning offers enhanced assessment of regional ventilation and perfusion heterogeneity.
Purpose of the Study:
- To validate and demonstrate the clinical applicability of deep learning-based CT lung segment parcellation.
- To assess the technique in a clinical cohort with mixed airway diseases.
Main Methods:
- A 3D convolutional neural network was used for airway centerline determination and tertiary bronchi identification.
- End-to-end lung segment parcellation was trained directly from chest CT images.
- Performance was evaluated using Dice score and inclusion rate on training data and qualitatively by radiologists on external validation data.
Main Results:
- Quantitative analysis showed a mean Dice score of 86.81 and inclusion rate of 0.75.
- Qualitative evaluation demonstrated high intra-reader agreement (99.2%).
- Patients with COPD exhibited greater segmentation mismatch compared to healthy individuals.
Conclusions:
- A deep learning algorithm successfully generates lung parcellation masks from CT scans.
- Encouraging quantitative and qualitative results support potential clinical use for pulmonary segment-level lung analysis.
- The method is particularly relevant for patients with structural airway disease.
