Extraction of Pulmonary Airway in CT Scans Using Deep Fully Convolutional Networks
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Abstract (arXiv)
Accurate, automatic and complete extraction of pulmonary airway in medical images plays an important role in analyzing thoracic CT volumes such as lung cancer detection, chronic obstructive pulmonary disease (COPD), and bronchoscopic-assisted surgery navigation. However, this task remains challenges, due to the complex tree-like structure of the airways. In this technical report, we use two-stage fully convolutional networks (FCNs) to automatically segment pulmonary airway in thoracic CT scans from multi-sites. Specifically, we firstly adopt a 3D FCN with U-shape network architecture to segment pulmonary airway in a coarse resolution in order to accelerate medical image analysis pipeline. And then another one 3D FCN is trained to segment pulmonary airway in a fine resolution. In the 2022 MICCAI Multi-site Multi-domain Airway Tree Modeling (ATM) Challenge, the reported method was evaluated on the public training set of 300 cases and independent private validation set of 50 cases. The resulting Dice Similarity Coefficient (DSC) is 0.914 ± 0.040, False Negative Error (FNE) is 0.079 ± 0.042, and False Positive Error (FPE) is 0.090 ± 0.066 on independent private validation set.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 27 references that are in the map. First posted on arXiv 12 Aug 2022.
Builds on
11 of 27 references.
References · 11
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
- 2020Alleviating Class-wise Gradient Imbalance for Pulmonary Airway Segmentationcited by 35
- 2019AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networkscited by 32
- 2020Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CTcited by 28
- 2021Automatic airway segmentation from Computed Tomography using robust and efficient 3-D convolutional neural networkscited by 18
- 2022NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation Pipelinecited by 17
- 2022BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embeddingcited by 16
- 2021FDA: Feature Decomposition and Aggregation for Robust Airway Segmentationcited by 12
- 2022LTSP: Long-Term Slice Propagation for Accurate Airway Segmentationcited by 6
- 2021Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growingcited by 4
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Citing papers per year: 2023: 2.
2 papers.