Balancing Accuracy and Interpretability in Automated 3D Lung Segmentation for Lung Disease: The Role of Classical Techniques

This study presents an automatic segmentation algorithm for accurately delineating lungs with pathological attenuations in 3D computed tomography (CT) images. Classical image processing methods are applied, including intrinsic image decomposition (IID) filtering and wavelet transform. Two contour refinement strategies - convex hull and corner detection - are evaluated, with the convex hull approach demonstrating superior performance, achieving a Dice similarity coefficient (DSC) of 98% against expert manual delineations. When compared to a state-of-the-art deep learning (DL) model (TotalSegmentatorV2), the classical approach remains competitive, achieving lower Hausdorff distance (HD), indicating fewer extreme segmentation errors. These results suggest that while DL methods provide high segmentation accuracy, classical approaches that combine 2D and 3D processing still offer advantages in mitigating outlier errors and ensuring interpretability. Additionally, the robustness and interpretability of classical methods make them ideal for generating accurate, well-annotated training datasets for DL models, enhancing performance, especially in lung disease contexts. Future work could explore hybrid models that leverage the strengths of both classical and DL-based techniques for robust lung segmentation.