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Automatic airway segmentation in chest CT using convolutional neural networks. / Juarez, A. Garcia Uceda; Tiddens, H. A.W.M.; de Bruijne, M.
Image Analysis for Moving Organ, Breast, and Thoracic Images: Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Proceedings. Springer, 2018. p. 238-250 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11040 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Juarez, AGU, Tiddens, HAWM
& de Bruijne, M 2018,
Automatic airway segmentation in chest CT using convolutional neural networks. in
Image Analysis for Moving Organ, Breast, and Thoracic Images: Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11040 LNCS, pp. 238-250, 3rd International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018, 4th International Workshop on Breast Image Analysis, BIA 2018, and 1st International Workshop on Thoracic Image Analysis, TIA 2018, held in conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, Granada, Spain,
16/09/2018.
https://doi.org/10.1007/978-3-030-00946-5_24APA
Juarez, A. G. U., Tiddens, H. A. W. M.
, & de Bruijne, M. (2018).
Automatic airway segmentation in chest CT using convolutional neural networks. In
Image Analysis for Moving Organ, Breast, and Thoracic Images: Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Proceedings (pp. 238-250). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11040 LNCS
https://doi.org/10.1007/978-3-030-00946-5_24Vancouver
Juarez AGU, Tiddens HAWM
, de Bruijne M.
Automatic airway segmentation in chest CT using convolutional neural networks. In Image Analysis for Moving Organ, Breast, and Thoracic Images: Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Proceedings. Springer. 2018. p. 238-250. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11040 LNCS).
https://doi.org/10.1007/978-3-030-00946-5_24Author
Juarez, A. Garcia Uceda ; Tiddens, H. A.W.M. ; de Bruijne, M. / Automatic airway segmentation in chest CT using convolutional neural networks. Image Analysis for Moving Organ, Breast, and Thoracic Images: Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Proceedings. Springer, 2018. pp. 238-250 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11040 LNCS).
Bibtex
@inproceedings{d8c5079cb9734c3388fc31fd73b021c2,
title = "Automatic airway segmentation in chest CT using convolutional neural networks",
abstract = "Segmentation of the airway tree from chest computed tomography (CT) images is critical for quantitative assessment of airway diseases including bronchiectasis and chronic obstructive pulmonary disease (COPD). However, obtaining an accurate segmentation of airways from CT scans is difficult due to the high complexity of airway structures. Recently, deep convolutional neural networks (CNNs) have become the state-of-the-art for many segmentation tasks, and in particular the so-called Unet architecture for biomedical images. However, its application to the segmentation of airways still remains a challenging task. This work presents a simple but robust approach based on a 3D Unet to perform segmentation of airways from chest CTs. The method is trained on a dataset composed of 12 CTs, and tested on another 6 CTs. We evaluate the influence of different loss functions and data augmentation techniques, and reach an average dice coefficient of 0.8 between the ground-truth and our automated segmentations.",
keywords = "Airway segmentation, Bronchiectasis, Convolutional neural networks, CT, Data augmentation",
author = "Juarez, {A. Garcia Uceda} and Tiddens, {H. A.W.M.} and {de Bruijne}, M.",
year = "2018",
doi = "10.1007/978-3-030-00946-5_24",
language = "English",
isbn = "9783030009458",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "238--250",
booktitle = "Image Analysis for Moving Organ, Breast, and Thoracic Images",
address = "Switzerland",
note = "3rd International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018, 4th International Workshop on Breast Image Analysis, BIA 2018, and 1st International Workshop on Thoracic Image Analysis, TIA 2018, held in conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018 ; Conference date: 16-09-2018 Through 20-09-2018",
}
RIS
TY - GEN
T1 - Automatic airway segmentation in chest CT using convolutional neural networks
AU - Juarez, A. Garcia Uceda
AU - Tiddens, H. A.W.M.
AU - de Bruijne, M.
PY - 2018
Y1 - 2018
N2 - Segmentation of the airway tree from chest computed tomography (CT) images is critical for quantitative assessment of airway diseases including bronchiectasis and chronic obstructive pulmonary disease (COPD). However, obtaining an accurate segmentation of airways from CT scans is difficult due to the high complexity of airway structures. Recently, deep convolutional neural networks (CNNs) have become the state-of-the-art for many segmentation tasks, and in particular the so-called Unet architecture for biomedical images. However, its application to the segmentation of airways still remains a challenging task. This work presents a simple but robust approach based on a 3D Unet to perform segmentation of airways from chest CTs. The method is trained on a dataset composed of 12 CTs, and tested on another 6 CTs. We evaluate the influence of different loss functions and data augmentation techniques, and reach an average dice coefficient of 0.8 between the ground-truth and our automated segmentations.
AB - Segmentation of the airway tree from chest computed tomography (CT) images is critical for quantitative assessment of airway diseases including bronchiectasis and chronic obstructive pulmonary disease (COPD). However, obtaining an accurate segmentation of airways from CT scans is difficult due to the high complexity of airway structures. Recently, deep convolutional neural networks (CNNs) have become the state-of-the-art for many segmentation tasks, and in particular the so-called Unet architecture for biomedical images. However, its application to the segmentation of airways still remains a challenging task. This work presents a simple but robust approach based on a 3D Unet to perform segmentation of airways from chest CTs. The method is trained on a dataset composed of 12 CTs, and tested on another 6 CTs. We evaluate the influence of different loss functions and data augmentation techniques, and reach an average dice coefficient of 0.8 between the ground-truth and our automated segmentations.
KW - Airway segmentation
KW - Bronchiectasis
KW - Convolutional neural networks
KW - CT
KW - Data augmentation
UR - http://www.scopus.com/inward/record.url?scp=85053890171&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00946-5_24
DO - 10.1007/978-3-030-00946-5_24
M3 - Article in proceedings
AN - SCOPUS:85053890171
SN - 9783030009458
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 238
EP - 250
BT - Image Analysis for Moving Organ, Breast, and Thoracic Images
PB - Springer
T2 - 3rd International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018, 4th International Workshop on Breast Image Analysis, BIA 2018, and 1st International Workshop on Thoracic Image Analysis, TIA 2018, held in conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -
