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

• URL: http://arxiv.org/abs/2511.14649v1
• Date: Tue, 18 Nov 2025 16:41:44 GMT
• Title: RepAir: A Framework for Airway Segmentation and Discontinuity Correction in CT
• Authors: John M. Oyer, Ali Namvar, Benjamin A. Hoff, Wassim W. Labaki, Ella A. Kazerooni, Charles R. Hatt, Fernando J. Martinez, MeiLan K. Han, Craig J. Galbán, Sundaresh Ram,
• Abstract summary: RepAir is a 3D airway segmentation framework that combines an nnU-Net-based network with anatomically informed topology correction.<n>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.
• Score: 30.786771148081446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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