{"ID":2840135,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.14649","arxiv_id":"2511.14649","title":"RepAir: A Framework for Airway Segmentation and Discontinuity Correction in CT","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.","short_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 3...","url_abs":"https://arxiv.org/abs/2511.14649","url_pdf":"https://arxiv.org/pdf/2511.14649v2","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\"]","published":"2025-11-18T16:41:44Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}