Standard

Reproducible white matter tract segmentation using 3D U-net on a large-scale DTI dataset. / Li, Bo; de Groot, Marius; Vernooij, Meike W.; Ikram, M. Arfan; Niessen, Wiro J.; Bron, Esther E.

Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. ed. / Yinghuan Shi; Heung-Il Suk; Mingxia Liu. Vol. 11046 LNCS Springer, 2018. p. 205-213 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11046 LNCS).

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Harvard

Li, B, de Groot, M, Vernooij, MW, Ikram, MA, Niessen, WJ & Bron, EE 2018, Reproducible white matter tract segmentation using 3D U-net on a large-scale DTI dataset. in Y Shi, H-I Suk & M Liu (eds), Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. vol. 11046 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11046 LNCS, Springer, pp. 205-213, 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, Granada, Spain, 16/09/18. https://doi.org/10.1007/978-3-030-00919-9_24

APA

Li, B., de Groot, M., Vernooij, M. W., Ikram, M. A., Niessen, W. J., & Bron, E. E. (2018). Reproducible white matter tract segmentation using 3D U-net on a large-scale DTI dataset. In Y. Shi, H-I. Suk, & M. Liu (Eds.), Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings (Vol. 11046 LNCS, pp. 205-213). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11046 LNCS). Springer. https://doi.org/10.1007/978-3-030-00919-9_24

Vancouver

Li B, de Groot M, Vernooij MW, Ikram MA, Niessen WJ, Bron EE. Reproducible white matter tract segmentation using 3D U-net on a large-scale DTI dataset. In Shi Y, Suk H-I, Liu M, editors, Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. Vol. 11046 LNCS. Springer. 2018. p. 205-213. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-00919-9_24

Author

Li, Bo ; de Groot, Marius ; Vernooij, Meike W. ; Ikram, M. Arfan ; Niessen, Wiro J. ; Bron, Esther E. / Reproducible white matter tract segmentation using 3D U-net on a large-scale DTI dataset. Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. editor / Yinghuan Shi ; Heung-Il Suk ; Mingxia Liu. Vol. 11046 LNCS Springer, 2018. pp. 205-213 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{a22a4f88bcac4de0af0a6cdbf96a7df2,
title = "Reproducible white matter tract segmentation using 3D U-net on a large-scale DTI dataset",
abstract = "Tract-specific diffusion measures, as derived from brain diffusion MRI, have been linked to white matter tract structural integrity and neurodegeneration. As a consequence, there is a large interest in the automatic segmentation of white matter tract in diffusion tensor MRI data. Methods based on the tractography are popular for white matter tract segmentation. However, because of the limited consistency and long processing time, such methods may not be suitable for clinical practice. We therefore developed a novel convolutional neural network based method to directly segment white matter tract trained on a low-resolution dataset of 9149 DTI images. The method is optimized on input, loss function and network architecture selections. We evaluated both segmentation accuracy and reproducibility, and reproducibility of determining tract-specific diffusion measures. The reproducibility of the method is higher than that of the reference standard and the determined diffusion measures are consistent. Therefore, we expect our method to be applicable in clinical practice and in longitudinal analysis of white matter microstructure.",
keywords = "3D, Convolution neural network, Diffusion measurements, DTI, Low resolution, Segmentation, Tract, White Matter",
author = "Bo Li and {de Groot}, Marius and Vernooij, {Meike W.} and Ikram, {M. Arfan} and Niessen, {Wiro J.} and Bron, {Esther E.}",
year = "2018",
doi = "10.1007/978-3-030-00919-9_24",
language = "English",
isbn = "9783030009182",
volume = "11046 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "205--213",
editor = "Shi, {Yinghuan } and Suk, {Heung-Il } and Liu, {Mingxia }",
booktitle = "Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings",

}

RIS

TY - GEN

T1 - Reproducible white matter tract segmentation using 3D U-net on a large-scale DTI dataset

AU - Li, Bo

AU - de Groot, Marius

AU - Vernooij, Meike W.

AU - Ikram, M. Arfan

AU - Niessen, Wiro J.

AU - Bron, Esther E.

PY - 2018

Y1 - 2018

N2 - Tract-specific diffusion measures, as derived from brain diffusion MRI, have been linked to white matter tract structural integrity and neurodegeneration. As a consequence, there is a large interest in the automatic segmentation of white matter tract in diffusion tensor MRI data. Methods based on the tractography are popular for white matter tract segmentation. However, because of the limited consistency and long processing time, such methods may not be suitable for clinical practice. We therefore developed a novel convolutional neural network based method to directly segment white matter tract trained on a low-resolution dataset of 9149 DTI images. The method is optimized on input, loss function and network architecture selections. We evaluated both segmentation accuracy and reproducibility, and reproducibility of determining tract-specific diffusion measures. The reproducibility of the method is higher than that of the reference standard and the determined diffusion measures are consistent. Therefore, we expect our method to be applicable in clinical practice and in longitudinal analysis of white matter microstructure.

AB - Tract-specific diffusion measures, as derived from brain diffusion MRI, have been linked to white matter tract structural integrity and neurodegeneration. As a consequence, there is a large interest in the automatic segmentation of white matter tract in diffusion tensor MRI data. Methods based on the tractography are popular for white matter tract segmentation. However, because of the limited consistency and long processing time, such methods may not be suitable for clinical practice. We therefore developed a novel convolutional neural network based method to directly segment white matter tract trained on a low-resolution dataset of 9149 DTI images. The method is optimized on input, loss function and network architecture selections. We evaluated both segmentation accuracy and reproducibility, and reproducibility of determining tract-specific diffusion measures. The reproducibility of the method is higher than that of the reference standard and the determined diffusion measures are consistent. Therefore, we expect our method to be applicable in clinical practice and in longitudinal analysis of white matter microstructure.

KW - 3D

KW - Convolution neural network

KW - Diffusion measurements

KW - DTI

KW - Low resolution

KW - Segmentation

KW - Tract

KW - White Matter

UR - http://www.scopus.com/inward/record.url?scp=85054487750&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-00919-9_24

DO - 10.1007/978-3-030-00919-9_24

M3 - Conference contribution

SN - 9783030009182

VL - 11046 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 205

EP - 213

BT - Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings

A2 - Shi, Yinghuan

A2 - Suk, Heung-Il

A2 - Liu, Mingxia

PB - Springer

ER -

ID: 47136933