Benefits of auxiliary information in deep learning-based teeth segmentation
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Benefits of auxiliary information in deep learning-based teeth segmentation. / Dascalu, Tudor Laurentiu; Kuznetsov, Artem; Ibragimov, Bulat.
Medical Imaging 2022: Image Processing. ed. / Olivier Colliot; Ivana Isgum; Bennett A. Landman; Murray H. Loew. SPIE, 2022. p. 1-9 1203232 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol. 12032).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Benefits of auxiliary information in deep learning-based teeth segmentation
AU - Dascalu, Tudor Laurentiu
AU - Kuznetsov, Artem
AU - Ibragimov, Bulat
N1 - Publisher Copyright: © 2022 SPIE.
PY - 2022
Y1 - 2022
N2 - This paper evaluates deep learning methods on segmentation of dental arches in panoramic radiographs. Our main aim is to test whether introducing auxiliary learning goals can improve image segmentation. We implement three multi-output networks that detect (1) patient characteristics (e.g missing teeth, no dental artifacts), (2) buccal area, (3) individual teeth, alongside the dental arches. These design choices may restrict the region of interest and improve the internal representation of teeth shapes. The models are based on the modified U-net1 architecture and optimized with Dice loss. Two data sets, of 1500 and 116 samples, collected at different institutions2, 3 were used for training and testing the methods. Additionally, we evaluated the networks against various patient conditions, namely: 32 teeth, ? 32 teeth, dental artifacts, no dental artifacts. The standard U-net architecture reaches the highest Dice scores of 0.932 on the larger data set2 and 0.946 on the group of patients with no missing teeth. The model that outputs probability masks for individual teeth reaches the best Dice score of 0.903 on the smaller data set.3 We observe certain benefits in augmenting teeth segmentation with other information sources, which indicate the potential of this research direction and justifies further investigations.
AB - This paper evaluates deep learning methods on segmentation of dental arches in panoramic radiographs. Our main aim is to test whether introducing auxiliary learning goals can improve image segmentation. We implement three multi-output networks that detect (1) patient characteristics (e.g missing teeth, no dental artifacts), (2) buccal area, (3) individual teeth, alongside the dental arches. These design choices may restrict the region of interest and improve the internal representation of teeth shapes. The models are based on the modified U-net1 architecture and optimized with Dice loss. Two data sets, of 1500 and 116 samples, collected at different institutions2, 3 were used for training and testing the methods. Additionally, we evaluated the networks against various patient conditions, namely: 32 teeth, ? 32 teeth, dental artifacts, no dental artifacts. The standard U-net architecture reaches the highest Dice scores of 0.932 on the larger data set2 and 0.946 on the group of patients with no missing teeth. The model that outputs probability masks for individual teeth reaches the best Dice score of 0.903 on the smaller data set.3 We observe certain benefits in augmenting teeth segmentation with other information sources, which indicate the potential of this research direction and justifies further investigations.
UR - http://www.scopus.com/inward/record.url?scp=85131959728&partnerID=8YFLogxK
U2 - 10.1117/12.2610765
DO - 10.1117/12.2610765
M3 - Article in proceedings
AN - SCOPUS:85131959728
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
SP - 1
EP - 9
BT - Medical Imaging 2022
A2 - Colliot, Olivier
A2 - Isgum, Ivana
A2 - Landman, Bennett A.
A2 - Loew, Murray H.
PB - SPIE
T2 - Medical Imaging 2022: Image Processing
Y2 - 21 March 2021 through 27 March 2021
ER -
ID: 314303065