RepAir: A Framework for Airway Segmentation and Discontinuity Correction in CT

TL;DR

RepAir is a three-stage framework that enhances airway segmentation in CT scans by combining deep learning with topology correction, leading to more accurate and anatomically consistent airway models.

Contribution

It introduces a novel three-stage method integrating nnU-Net, skeleton analysis, and topology correction for improved airway segmentation.

Findings

Outperforms existing methods on multiple datasets

Produces more complete and anatomically accurate airway trees

Maintains high segmentation accuracy

Abstract

Accurate airway segmentation from chest computed tomography (CT) scans is essential for quantitative lung analysis, yet manual annotation is impractical and many automated U-Net-based methods yield disconnected components that hinder reliable biomarker extraction. We present RepAir, a three-stage framework for robust 3D airway segmentation that combines an nnU-Net-based network with anatomically informed topology correction. The segmentation network produces an initial airway mask, after which a skeleton-based algorithm identifies potential discontinuities and proposes reconnections. A 1D convolutional classifier then determines which candidate links correspond to true anatomical branches versus false or obstructed paths. We evaluate RepAir on two distinct datasets: ATM'22, comprising annotated CT scans from predominantly healthy subjects and AeroPath, encompassing annotated scans with severe airway pathology. Across both datasets, RepAir outperforms existing 3D U-Net-based approaches such as Bronchinet and NaviAirway on both voxel-level and topological metrics, and produces more complete and anatomically consistent airway trees while maintaining high segmentation accuracy.