Standard
Automated lesion detection by regressing intensity-based distance with a neural network. / van Wijnen, Kimberlin M.H.; Dubost, Florian; Yilmaz, Pinar; Ikram, M. Arfan; Niessen, Wiro J.; Adams, Hieab; Vernooij, Meike W.; de Bruijne, Marleen.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. ed. / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Springer VS, 2019. p. 234-242 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11767 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
van Wijnen, KMH, Dubost, F, Yilmaz, P, Ikram, MA, Niessen, WJ, Adams, H, Vernooij, MW
& de Bruijne, M 2019,
Automated lesion detection by regressing intensity-based distance with a neural network. in D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (eds),
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer VS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11767 LNCS, pp. 234-242, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China,
13/10/2019.
https://doi.org/10.1007/978-3-030-32251-9_26
APA
van Wijnen, K. M. H., Dubost, F., Yilmaz, P., Ikram, M. A., Niessen, W. J., Adams, H., Vernooij, M. W.
, & de Bruijne, M. (2019).
Automated lesion detection by regressing intensity-based distance with a neural network. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.),
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 234-242). Springer VS. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11767 LNCS
https://doi.org/10.1007/978-3-030-32251-9_26
Vancouver
van Wijnen KMH, Dubost F, Yilmaz P, Ikram MA, Niessen WJ, Adams H et al.
Automated lesion detection by regressing intensity-based distance with a neural network. In Shen D, Yap P-T, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer VS. 2019. p. 234-242. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11767 LNCS).
https://doi.org/10.1007/978-3-030-32251-9_26
Author
van Wijnen, Kimberlin M.H. ; Dubost, Florian ; Yilmaz, Pinar ; Ikram, M. Arfan ; Niessen, Wiro J. ; Adams, Hieab ; Vernooij, Meike W. ; de Bruijne, Marleen. / Automated lesion detection by regressing intensity-based distance with a neural network. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. editor / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Springer VS, 2019. pp. 234-242 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11767 LNCS).
Bibtex
@inproceedings{7d6b249091b547fd936d308de17003c2,
title = "Automated lesion detection by regressing intensity-based distance with a neural network",
abstract = "Localization of focal vascular lesions on brain MRI is an important component of research on the etiology of neurological disorders. However, manual annotation of lesions can be challenging, time-consuming and subject to observer bias. Automated detection methods often need voxel-wise annotations for training. We propose a novel approach for automated lesion detection that can be trained on scans only annotated with a dot per lesion instead of a full segmentation. From the dot annotations and their corresponding intensity images we compute various distance maps (DMs), indicating the distance to a lesion based on spatial distance, intensity distance, or both. We train a fully convolutional neural network (FCN) to predict these DMs for unseen intensity images. The local optima in the predicted DMs are expected to correspond to lesion locations. We show the potential of this approach to detect enlarged perivascular spaces in white matter on a large brain MRI dataset with an independent test set of 1000 scans. Our method matches the intra-rater performance of the expert rater that was computed on an independent set. We compare the different types of distance maps, showing that incorporating intensity information in the distance maps used to train an FCN greatly improves performance.",
keywords = "Dot annotations, Fully convolutional neural network, Geodesic distance, Lesion detection, Perivascular spaces",
author = "{van Wijnen}, {Kimberlin M.H.} and Florian Dubost and Pinar Yilmaz and Ikram, {M. Arfan} and Niessen, {Wiro J.} and Hieab Adams and Vernooij, {Meike W.} and {de Bruijne}, Marleen",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-32251-9_26",
language = "English",
isbn = "9783030322502",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer VS",
pages = "234--242",
editor = "Dinggang Shen and Pew-Thian Yap and Tianming Liu and Peters, {Terry M.} and Ali Khan and Staib, {Lawrence H.} and Caroline Essert and Sean Zhou",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings",
note = "22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",
}
RIS
TY - GEN
T1 - Automated lesion detection by regressing intensity-based distance with a neural network
AU - van Wijnen, Kimberlin M.H.
AU - Dubost, Florian
AU - Yilmaz, Pinar
AU - Ikram, M. Arfan
AU - Niessen, Wiro J.
AU - Adams, Hieab
AU - Vernooij, Meike W.
AU - de Bruijne, Marleen
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Localization of focal vascular lesions on brain MRI is an important component of research on the etiology of neurological disorders. However, manual annotation of lesions can be challenging, time-consuming and subject to observer bias. Automated detection methods often need voxel-wise annotations for training. We propose a novel approach for automated lesion detection that can be trained on scans only annotated with a dot per lesion instead of a full segmentation. From the dot annotations and their corresponding intensity images we compute various distance maps (DMs), indicating the distance to a lesion based on spatial distance, intensity distance, or both. We train a fully convolutional neural network (FCN) to predict these DMs for unseen intensity images. The local optima in the predicted DMs are expected to correspond to lesion locations. We show the potential of this approach to detect enlarged perivascular spaces in white matter on a large brain MRI dataset with an independent test set of 1000 scans. Our method matches the intra-rater performance of the expert rater that was computed on an independent set. We compare the different types of distance maps, showing that incorporating intensity information in the distance maps used to train an FCN greatly improves performance.
AB - Localization of focal vascular lesions on brain MRI is an important component of research on the etiology of neurological disorders. However, manual annotation of lesions can be challenging, time-consuming and subject to observer bias. Automated detection methods often need voxel-wise annotations for training. We propose a novel approach for automated lesion detection that can be trained on scans only annotated with a dot per lesion instead of a full segmentation. From the dot annotations and their corresponding intensity images we compute various distance maps (DMs), indicating the distance to a lesion based on spatial distance, intensity distance, or both. We train a fully convolutional neural network (FCN) to predict these DMs for unseen intensity images. The local optima in the predicted DMs are expected to correspond to lesion locations. We show the potential of this approach to detect enlarged perivascular spaces in white matter on a large brain MRI dataset with an independent test set of 1000 scans. Our method matches the intra-rater performance of the expert rater that was computed on an independent set. We compare the different types of distance maps, showing that incorporating intensity information in the distance maps used to train an FCN greatly improves performance.
KW - Dot annotations
KW - Fully convolutional neural network
KW - Geodesic distance
KW - Lesion detection
KW - Perivascular spaces
U2 - 10.1007/978-3-030-32251-9_26
DO - 10.1007/978-3-030-32251-9_26
M3 - Article in proceedings
AN - SCOPUS:85075694039
SN - 9783030322502
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 234
EP - 242
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer VS
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -