mlsanityData to 14 Oct 2026

Adversarial Transformer for Repairing Human Airway Segmentation

arXiv 2210.12029 ↗First posted 21 Oct 2022eess.IV cs.CVcited by 4

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Abstract (arXiv)

Discontinuity in the delineation of peripheral bronchioles hinders the potential clinical application of automated airway segmentation models. Moreover, the deployment of such models is limited by the data heterogeneity across different centres, and pathological abnormalities also make achieving accurate robust segmentation in distal small airways difficult. Meanwhile, the diagnosis and prognosis of lung diseases often rely on evaluating structural changes in those anatomical regions. To address this gap, this paper presents a patch-scale adversarial-based refinement network that takes in preliminary segmentation along with original CT images and outputs a refined mask of the airway structure. The method is validated on three different datasets encompassing healthy cases, cases with cystic fibrosis and cases with COVID-19. The results are quantitatively evaluated by seven metrics and achieved more than a 15% rise in detected length ratio and detected branch ratio, showing promising performance compared to previously proposed models. The visual illustration also proves our refinement guided by a patch-scale discriminator and centreline objective functions is effective in detecting discontinuities and missing bronchioles. Furthermore, the generalizability of our refinement pipeline is tested on three previous models and improves their segmentation completeness significantly.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 15 of its 33 references that are in the map. First posted on arXiv 21 Oct 2022.

Builds on

15 of 33 references.

References · 15

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3 more
  1. 2018Automatic Airway Segmentation in chest CT using Convolutional Neural Networkscited by 18
  2. 2022Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentationcited by 18
  3. 2022NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation Pipelinecited by 17

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2023AeroPath: An airway segmentation benchmark dataset with challenging pathologycited by 10
  2. 2024Intensity Confusion Matters: An Intensity-Distance Guided Loss for Bronchus Segmentationcited by 6
  3. 2024Fuzzy Attention-based Border Rendering Network for Lung Organ Segmentationcited by 0
  4. 2025RepAir: A Framework for Airway Segmentation and Discontinuity Correction in CTcited by 0