Accurate segmentation of dental panoramic radiographs with u-NETS

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedings

Standard

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/rapportKonferencebidrag i proceedings

Harvard

Koch, TL, Perslev, M, Igel, C & Brandt, SS 2019, Accurate segmentation of dental panoramic radiographs with u-NETS. i ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging. IEEE, s. 15-19, 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, Venice, Italien, 08/04/2019. https://doi.org/10.1109/ISBI.2019.8759563

APA

Koch, T. L., Perslev, M., Igel, C., & Brandt, S. S. (2019). Accurate segmentation of dental panoramic radiographs with u-NETS. I ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging (s. 15-19). IEEE. https://doi.org/10.1109/ISBI.2019.8759563

Vancouver

Koch TL, Perslev M, Igel C, Brandt SS. Accurate segmentation of dental panoramic radiographs with u-NETS. I ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging. IEEE. 2019. s. 15-19 https://doi.org/10.1109/ISBI.2019.8759563

Author

Koch, Thorbjorn Louring ; Perslev, Mathis ; Igel, Christian ; Brandt, Sami Sebastian. / Accurate segmentation of dental panoramic radiographs with u-NETS. ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging. IEEE, 2019. s. 15-19

Bibtex

@inproceedings{967fee898e1c48c181306435c1aa0ce0,
title = "Accurate segmentation of dental panoramic radiographs with u-NETS",
abstract = "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.",
keywords = "Deep learning, Dental radiography, Fully convolutional neural network, Pantomogram, Segmentation",
author = "Koch, {Thorbjorn Louring} and Mathis Perslev and Christian Igel and Brandt, {Sami Sebastian}",
year = "2019",
doi = "10.1109/ISBI.2019.8759563",
language = "English",
pages = "15--19",
booktitle = "ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging",
publisher = "IEEE",
note = "16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 ; Conference date: 08-04-2019 Through 11-04-2019",

}

RIS

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