AirMorph: Topology-Preserving Deep Learning for Pulmonary Airway Analysis

TL;DR

AirMorph is a deep learning pipeline that automatically labels pulmonary airways at multiple resolutions, creating detailed anatomical atlases and extracting morphological features to aid diagnosis and treatment of lung diseases.

Contribution

This work introduces AirMorph, a novel end-to-end deep learning method for comprehensive, topology-preserving airway segmentation and morphological analysis from thoracic CT scans.

Findings

Outperforms existing methods in accuracy and topological consistency

Effectively characterizes disease-specific airway patterns

Supports clinical applications like navigation and diagnosis

Abstract

Accurate anatomical labeling and analysis of the pulmonary structure and its surrounding anatomy from thoracic CT is getting increasingly important for understanding the etilogy of abnormalities or supporting targetted therapy and early interventions. Whilst lung and airway cell atlases have been attempted, there is a lack of fine-grained morphological atlases that are clinically deployable. In this work, we introduce AirMorph, a robust, end-to-end deep learning pipeline enabling fully automatic and comprehensive airway anatomical labeling at lobar, segmental, and subsegmental resolutions that can be used to create digital atlases of the lung. Evaluated across large-scale multi-center datasets comprising diverse pulmonary conditions, the AirMorph consistently outperformed existing segmentation and labeling methods in terms of accuracy, topological consistency, and completeness. To simplify clinical interpretation, we further introduce a compact anatomical signature quantifying critical morphological airway features, including stenosis, ectasia, tortuosity, divergence, length, and complexity. When applied to various pulmonary diseases such as pulmonary fibrosis, emphysema, atelectasis, consolidation, and reticular opacities, it demonstrates strong discriminative power, revealing disease-specific morphological patterns with high interpretability and explainability. Additionally, AirMorph supports efficient automated branching pattern analysis, potentially enhancing bronchoscopic navigation planning and procedural safety, offering a valuable clinical tool for improved diagnosis, targeted treatment, and personalized patient care.