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Updated: May 28, 2026
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
A deep learning lung cancer segmentation pipeline to facilitate CT-based radiomics
A C P So1, D Cheng2, S Aslani3
1Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; Satsuma Lab, Hawke's Institute, University College London, London, United Kingdom.
Aim:
CT-based radio-biomarkers could provide non-invasive insights into tumour biology to risk-stratify patients. One of the limitations is the laborious manual segmentation of regions-of-interest (ROI). We present a deep learning auto-segmentation pipeline for radiomic analysis.
Materials And Methods:
153 patients with resected stage 2A-3B non-small cell lung cancer (NSCLC) had tumours segmented using nnU-Net with review by two clinicians. The nnU-Net was pretrained with anatomical priors in non-cancerous lungs and finetuned on NSCLCs. Three ROIs were segmented: intra-tumoural, peri-tumoural, and whole lung. A total of 1967 features were extracted using PyRadiomics. Feature reproducibility was tested using segmentation perturbations. Features were selected using minimum-redundancy-maximum-relevance with Random Forest-recursive feature elimination nested in 500 bootstraps.
Results:
Auto-segmentation time was ∼36 seconds/series. Mean volumetric and surface Dice-Sørensen coefficient (DSC) scores were 0.84 (±0.28), and 0.79 (±0.34) respectively. DSC scores were significantly correlated with tumour shape (sphericity, diameter) and location (with worse performance associated with chest wall adherence), but not with batch effects (e.g. reconstruction kernel). Overall, 6.5% of cases had 'missed' segmentations; 6.5% required major changes. Pre-training on anatomical priors resulted in better segmentations compared to training on tumour-labels alone (p<0.001) and tumour plus anatomical labels (p<0.001). Most radiomic features were not reproducible following perturbations and resampling. Adding radiomic features did not significantly improve the clinical model in predicting 2-year disease-free survival: AUCs were 0.67 (95%CI 0.59-0.75) vs 0.63 (95%CI 0.54-0.71) respectively (p=0.28).
Conclusion:
Our study demonstrates that integrating auto-segmentation into radio-biomarker discovery is feasible with high efficiency and accuracy. Whilst traditional radiomics show limited reproducibility, our auto-segmentation pipeline can facilitate future novel radio-biomarker development.