Creating a training set for artificial intelligence from initial segmentations of airways
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Creating a training set for artificial intelligence from initial segmentations of airways. / Dudurych, Ivan; Garcia-Uceda, Antonio; Saghir, Zaigham; Tiddens, Harm A.W.M.; Vliegenthart, Rozemarijn; de Bruijne, Marleen.
I: European radiology experimental, Bind 5, Nr. 1, 54, 2021.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Creating a training set for artificial intelligence from initial segmentations of airways
AU - Dudurych, Ivan
AU - Garcia-Uceda, Antonio
AU - Saghir, Zaigham
AU - Tiddens, Harm A.W.M.
AU - Vliegenthart, Rozemarijn
AU - de Bruijne, Marleen
N1 - Publisher Copyright: © 2021, The Author(s).
PY - 2021
Y1 - 2021
N2 - Airways segmentation is important for research about pulmonary disease but require a large amount of time by trained specialists. We used an openly available software to improve airways segmentations obtained from an artificial intelligence (AI) tool and retrained the tool to get a better performance. Fifteen initial airway segmentations from low-dose chest computed tomography scans were obtained with a 3D-Unet AI tool previously trained on Danish Lung Cancer Screening Trial and Erasmus-MC Sophia datasets. Segmentations were manually corrected in 3D Slicer. The corrected airway segmentations were used to retrain the 3D-Unet. Airway measurements were automatically obtained and included count, airway length and luminal diameter per generation from the segmentations. Correcting segmentations required 2–4 h per scan. Manually corrected segmentations had more branches (p < 0.001), longer airways (p < 0.001) and smaller luminal diameters (p = 0.004) than initial segmentations. Segmentations from retrained 3D-Unets trended towards more branches and longer airways compared to the initial segmentations. The largest changes were seen in airways from 6th generation onwards. Manual correction results in significantly improved segmentations and is potentially a useful and time-efficient method to improve the AI tool performance on a specific hospital or research dataset.
AB - Airways segmentation is important for research about pulmonary disease but require a large amount of time by trained specialists. We used an openly available software to improve airways segmentations obtained from an artificial intelligence (AI) tool and retrained the tool to get a better performance. Fifteen initial airway segmentations from low-dose chest computed tomography scans were obtained with a 3D-Unet AI tool previously trained on Danish Lung Cancer Screening Trial and Erasmus-MC Sophia datasets. Segmentations were manually corrected in 3D Slicer. The corrected airway segmentations were used to retrain the 3D-Unet. Airway measurements were automatically obtained and included count, airway length and luminal diameter per generation from the segmentations. Correcting segmentations required 2–4 h per scan. Manually corrected segmentations had more branches (p < 0.001), longer airways (p < 0.001) and smaller luminal diameters (p = 0.004) than initial segmentations. Segmentations from retrained 3D-Unets trended towards more branches and longer airways compared to the initial segmentations. The largest changes were seen in airways from 6th generation onwards. Manual correction results in significantly improved segmentations and is potentially a useful and time-efficient method to improve the AI tool performance on a specific hospital or research dataset.
KW - Artificial intelligence
KW - Image processing (computer-assisted)
KW - Respiratory system
KW - Thorax
KW - Tomography (x-ray computed)
U2 - 10.1186/s41747-021-00247-9
DO - 10.1186/s41747-021-00247-9
M3 - Journal article
C2 - 34841480
AN - SCOPUS:85120174029
VL - 5
JO - European radiology experimental
JF - European radiology experimental
SN - 2509-9280
IS - 1
M1 - 54
ER -
ID: 286989935