mlsanityData to 14 Oct 2026

Boundary-Emphasized Weight Maps for Distal Airway Segmentation

arXiv 2503.00242 ↗First posted 28 Feb 2025eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Automated airway segmentation from lung CT scans is vital for diagnosing and monitoring pulmonary diseases. Despite advancements, challenges like leakage, breakage, and class imbalance persist, particularly in capturing small airways and preserving topology. We propose the Boundary-Emphasized Loss (BEL), which enhances boundary preservation using a boundary-based weight map and an adaptive weight refinement strategy. Unlike centerline-based approaches, BEL prioritizes boundary voxels to reduce misclassification, improve topology, and enhance structural consistency, especially on distal airway branches. Evaluated on ATM22 and AIIB23, BEL outperforms baseline loss functions, achieving higher topology-related metrics and comparable overall-based measures. Qualitative results further highlight BEL's ability to capture fine anatomical details and reduce segmentation errors, particularly in small airways. These findings establish BEL as a promising solution for accurate and topology-enhancing airway segmentation in medical imaging.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 28 Feb 2025.

Builds on

16 of 33 references.

References · 16

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  4. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  5. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
  6. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
  7. 2020clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentationcited by 294
  8. 2017Tversky loss function for image segmentation using 3D fully convolutional deep networkscited by 228
  9. 2018A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentationcited by 172
  10. 2020Alleviating Class-wise Gradient Imbalance for Pulmonary Airway Segmentationcited by 35
  11. 2019AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networkscited by 32
  12. 2018Automatic Airway Segmentation in chest CT using Convolutional Neural Networkscited by 18
4 more
  1. 2021Automatic airway segmentation from Computed Tomography using robust and efficient 3-D convolutional neural networkscited by 18
  2. 2021FDA: Feature Decomposition and Aggregation for Robust Airway Segmentationcited by 12
  3. 2023Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challengecited by 12
  4. 2022Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentationcited by 3

Built on by

Not cited yet.