Extraction of airways from CT (EXACT'09)
- PMID: 22855226
- DOI: 10.1109/TMI.2012.2209674
Extraction of airways from CT (EXACT'09)
Abstract
This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.
Similar articles
-
Optimizing parameters of an open-source airway segmentation algorithm using different CT images.Biomed Eng Online. 2015 Jun 26;14:62. doi: 10.1186/s12938-015-0060-2. Biomed Eng Online. 2015. PMID: 26112975 Free PMC article.
-
Graph-Based Airway Tree Reconstruction From Chest CT Scans: Evaluation of Different Features on Five Cohorts.IEEE Trans Med Imaging. 2015 May;34(5):1063-76. doi: 10.1109/TMI.2014.2374615. Epub 2014 Nov 25. IEEE Trans Med Imaging. 2015. PMID: 25438305 Free PMC article.
-
[3D region growing algorithm driven by morphological dilation for airway tree segmentation in image guided therapy].Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2013 Aug;30(4):679-83, 691. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2013. PMID: 24059036 Chinese.
-
Robust segmentation and anatomical labeling of the airway tree from thoracic CT scans.Med Image Comput Comput Assist Interv. 2008;11(Pt 1):219-26. doi: 10.1007/978-3-540-85988-8_27. Med Image Comput Comput Assist Interv. 2008. PMID: 18979751
-
Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images.IEEE Trans Med Imaging. 2003 Aug;22(8):940-50. doi: 10.1109/TMI.2003.815905. IEEE Trans Med Imaging. 2003. PMID: 12906248
Cited by
-
Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks.PLoS One. 2024 Jan 26;19(1):e0297437. doi: 10.1371/journal.pone.0297437. eCollection 2024. PLoS One. 2024. PMID: 38277381 Free PMC article.
-
Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution.Diagnostics (Basel). 2023 Jun 25;13(13):2161. doi: 10.3390/diagnostics13132161. Diagnostics (Basel). 2023. PMID: 37443556 Free PMC article.
-
Deep anatomy learning for lung airway and artery-vein modeling with contrast-enhanced CT synthesis.Int J Comput Assist Radiol Surg. 2023 Jul;18(7):1287-1294. doi: 10.1007/s11548-023-02946-7. Epub 2023 May 31. Int J Comput Assist Radiol Surg. 2023. PMID: 37259009
-
Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction.Eur Radiol. 2023 Oct;33(10):6718-6725. doi: 10.1007/s00330-023-09615-y. Epub 2023 Apr 18. Eur Radiol. 2023. PMID: 37071168 Free PMC article.
-
RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion.J Digit Imaging. 2023 Jun;36(3):1248-1261. doi: 10.1007/s10278-022-00769-7. Epub 2023 Jan 26. J Digit Imaging. 2023. PMID: 36702987 Free PMC article.
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical