mlsanityData to 14 Oct 2026

Interpolation-Split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance

arXiv 2308.00008 ↗First posted 29 Jul 2023eess.IV cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

The morphology and distribution of airway tree abnormalities enables diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity. In this study, we propose a data-centric deep learning technique to segment the airway tree. The proposed technique utilises interpolation and image split to improve data usefulness and quality. Then, an ensemble learning strategy is implemented to aggregate the segmented airway trees at different scales. In terms of segmentation performance (dice similarity coefficient), our method outperforms the baseline model by 2.5% on average when a combined loss is used. Further, our proposed technique has a low GPU usage and high flexibility enabling it to be deployed on any 2D deep learning model.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 51 references that are in the map. First posted on arXiv 29 Jul 2023.

Builds on

20 of 51 references.

References · 20

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  3. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,487
  4. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,133
  5. 2019High-Resolution Representations for Labeling Pixels and Regionscited by 211
  6. 2019A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysiscited by 151
  7. 2022Image Data Augmentation for Deep Learning: A Surveycited by 96
  8. 2020Alleviating Class-wise Gradient Imbalance for Pulmonary Airway Segmentationcited by 35
  9. 2019A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTscited by 27
  10. 2021Automatic airway segmentation from Computed Tomography using robust and efficient 3-D convolutional neural networkscited by 18
  11. 2022NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation Pipelinecited by 17
  12. 2021Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanismscited by 9
8 more
  1. 2023Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentationcited by 9
  2. 2023Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learningcited by 7
  3. 2022LTSP: Long-Term Slice Propagation for Accurate Airway Segmentationcited by 6
  4. 2023Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Datasetcited by 6
  5. 2021Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growingcited by 4
  6. 2022Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentationcited by 3
  7. 2023GDDS: Pulmonary Bronchioles Segmentation with Group Deep Dense Supervisioncited by 3
  8. 2021Evaluation of automated airway morphological quantification for assessing fibrosing lung diseasecited by 2

Built on by

Not cited yet.