Accurate segmentation of dental panoramic radiographs with u-NETS
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Accurate segmentation of dental panoramic radiographs with u-NETS. / Koch, Thorbjorn Louring; Perslev, Mathis; Igel, Christian; Brandt, Sami Sebastian.
ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging. IEEE, 2019. s. 15-19.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Accurate segmentation of dental panoramic radiographs with u-NETS
AU - Koch, Thorbjorn Louring
AU - Perslev, Mathis
AU - Igel, Christian
AU - Brandt, Sami Sebastian
PY - 2019
Y1 - 2019
N2 - Fully convolutional neural networks (FCNs) have proven to be powerful tools for medical image segmentation. We apply an FCN based on the U-Net architecture for the challenging task of semantic segmentation of dental panoramic radiographs and discuss general tricks for improving segmentation performance. Among those are network ensembling, test-time augmentation, data symmetry exploitation and bootstrapping of low quality annotations. The performance of our approach was tested on a highly variable dataset of 1500 dental panoramic radiographs. A single network reached the Dice score of 0.934 where 1201 images were used for training, forming an ensemble increased the score to 0.936.
AB - Fully convolutional neural networks (FCNs) have proven to be powerful tools for medical image segmentation. We apply an FCN based on the U-Net architecture for the challenging task of semantic segmentation of dental panoramic radiographs and discuss general tricks for improving segmentation performance. Among those are network ensembling, test-time augmentation, data symmetry exploitation and bootstrapping of low quality annotations. The performance of our approach was tested on a highly variable dataset of 1500 dental panoramic radiographs. A single network reached the Dice score of 0.934 where 1201 images were used for training, forming an ensemble increased the score to 0.936.
KW - Deep learning
KW - Dental radiography
KW - Fully convolutional neural network
KW - Pantomogram
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85073091535&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2019.8759563
DO - 10.1109/ISBI.2019.8759563
M3 - Article in proceedings
AN - SCOPUS:85073091535
SP - 15
EP - 19
BT - ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PB - IEEE
T2 - 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Y2 - 8 April 2019 through 11 April 2019
ER -
ID: 230481516