Standard
Bundle Geodesic Convolutional Neural Network for DWI Segmentation from Single Scan Learning. / Liu, Renfei; Lauze, Francois; Erleben, Kenny; Darkner, Sune.
Computational Diffusion MRI - 12th International Workshop, CDMRI 2021, Held in Conjunction with MICCAI 2021, Proceedings. red. / Suheyla Cetin-Karayumak; Daan Christiaens; Matteo Figini; Pamela Guevara; Noemi Gyori; Vishwesh Nath; Tomasz Pieciak. Springer, 2021. s. 121-132 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 13006 LNCS).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Liu, R, Lauze, F, Erleben, K & Darkner, S 2021,
Bundle Geodesic Convolutional Neural Network for DWI Segmentation from Single Scan Learning. i S Cetin-Karayumak, D Christiaens, M Figini, P Guevara, N Gyori, V Nath & T Pieciak (red),
Computational Diffusion MRI - 12th International Workshop, CDMRI 2021, Held in Conjunction with MICCAI 2021, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 13006 LNCS, s. 121-132, 12th International Workshop on Computational Diffusion MRI, CDMRI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online,
01/10/2021.
https://doi.org/10.1007/978-3-030-87615-9_11
APA
Liu, R., Lauze, F., Erleben, K., & Darkner, S. (2021).
Bundle Geodesic Convolutional Neural Network for DWI Segmentation from Single Scan Learning. I S. Cetin-Karayumak, D. Christiaens, M. Figini, P. Guevara, N. Gyori, V. Nath, & T. Pieciak (red.),
Computational Diffusion MRI - 12th International Workshop, CDMRI 2021, Held in Conjunction with MICCAI 2021, Proceedings (s. 121-132). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 13006 LNCS
https://doi.org/10.1007/978-3-030-87615-9_11
Vancouver
Liu R, Lauze F, Erleben K, Darkner S.
Bundle Geodesic Convolutional Neural Network for DWI Segmentation from Single Scan Learning. I Cetin-Karayumak S, Christiaens D, Figini M, Guevara P, Gyori N, Nath V, Pieciak T, red., Computational Diffusion MRI - 12th International Workshop, CDMRI 2021, Held in Conjunction with MICCAI 2021, Proceedings. Springer. 2021. s. 121-132. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 13006 LNCS).
https://doi.org/10.1007/978-3-030-87615-9_11
Author
Liu, Renfei ; Lauze, Francois ; Erleben, Kenny ; Darkner, Sune. / Bundle Geodesic Convolutional Neural Network for DWI Segmentation from Single Scan Learning. Computational Diffusion MRI - 12th International Workshop, CDMRI 2021, Held in Conjunction with MICCAI 2021, Proceedings. red. / Suheyla Cetin-Karayumak ; Daan Christiaens ; Matteo Figini ; Pamela Guevara ; Noemi Gyori ; Vishwesh Nath ; Tomasz Pieciak. Springer, 2021. s. 121-132 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 13006 LNCS).
Bibtex
@inproceedings{2d28a9bbfddf40a799dc8f7fd7e38c94,
title = "Bundle Geodesic Convolutional Neural Network for DWI Segmentation from Single Scan Learning",
abstract = "We present a tissue classifier for Magnetic Resonance Diffusion Weighted Imaging (DWI) data trained from a single subject with a single b-value. The classifier is based on a Riemannian Deep Learning framework for extracting features with rotational invariance, where we extend a G-CNN learning architecture generically on a Riemannian manifold. We validate our framework using single-shell DWI data with a very limited amount of training data - only 1 scan. The proposed framework mainly consists of three layers: a lifting layer that locally represents and convolves data on tangent spaces to produce a family of functions defined on the rotation groups of the tangent spaces, i.e., a section of a bundle of rotational functions on the manifold; a group convolution layer that convolves this section with rotation kernels to produce a new section; and a projection layer using maximisation to collapse this local data to form new manifold based functions. We present an instantiation on the 2-dimensional sphere where the DWI orientation data is in general represented, and we use it for voxel classification. We show that this allows us to learn a classifier for cerebrospinal fluid (CSF) - subcortical - grey matter - white matter classification from only one scan.",
keywords = "Classification, DWI, Geodesic CNN, Single scan learning",
author = "Renfei Liu and Francois Lauze and Kenny Erleben and Sune Darkner",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 12th International Workshop on Computational Diffusion MRI, CDMRI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 01-10-2021 Through 01-10-2021",
year = "2021",
doi = "10.1007/978-3-030-87615-9_11",
language = "English",
isbn = "9783030876142",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "121--132",
editor = "Suheyla Cetin-Karayumak and Daan Christiaens and Matteo Figini and Pamela Guevara and Noemi Gyori and Vishwesh Nath and Tomasz Pieciak",
booktitle = "Computational Diffusion MRI - 12th International Workshop, CDMRI 2021, Held in Conjunction with MICCAI 2021, Proceedings",
address = "Switzerland",
}
RIS
TY - GEN
T1 - Bundle Geodesic Convolutional Neural Network for DWI Segmentation from Single Scan Learning
AU - Liu, Renfei
AU - Lauze, Francois
AU - Erleben, Kenny
AU - Darkner, Sune
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - We present a tissue classifier for Magnetic Resonance Diffusion Weighted Imaging (DWI) data trained from a single subject with a single b-value. The classifier is based on a Riemannian Deep Learning framework for extracting features with rotational invariance, where we extend a G-CNN learning architecture generically on a Riemannian manifold. We validate our framework using single-shell DWI data with a very limited amount of training data - only 1 scan. The proposed framework mainly consists of three layers: a lifting layer that locally represents and convolves data on tangent spaces to produce a family of functions defined on the rotation groups of the tangent spaces, i.e., a section of a bundle of rotational functions on the manifold; a group convolution layer that convolves this section with rotation kernels to produce a new section; and a projection layer using maximisation to collapse this local data to form new manifold based functions. We present an instantiation on the 2-dimensional sphere where the DWI orientation data is in general represented, and we use it for voxel classification. We show that this allows us to learn a classifier for cerebrospinal fluid (CSF) - subcortical - grey matter - white matter classification from only one scan.
AB - We present a tissue classifier for Magnetic Resonance Diffusion Weighted Imaging (DWI) data trained from a single subject with a single b-value. The classifier is based on a Riemannian Deep Learning framework for extracting features with rotational invariance, where we extend a G-CNN learning architecture generically on a Riemannian manifold. We validate our framework using single-shell DWI data with a very limited amount of training data - only 1 scan. The proposed framework mainly consists of three layers: a lifting layer that locally represents and convolves data on tangent spaces to produce a family of functions defined on the rotation groups of the tangent spaces, i.e., a section of a bundle of rotational functions on the manifold; a group convolution layer that convolves this section with rotation kernels to produce a new section; and a projection layer using maximisation to collapse this local data to form new manifold based functions. We present an instantiation on the 2-dimensional sphere where the DWI orientation data is in general represented, and we use it for voxel classification. We show that this allows us to learn a classifier for cerebrospinal fluid (CSF) - subcortical - grey matter - white matter classification from only one scan.
KW - Classification
KW - DWI
KW - Geodesic CNN
KW - Single scan learning
U2 - 10.1007/978-3-030-87615-9_11
DO - 10.1007/978-3-030-87615-9_11
M3 - Article in proceedings
AN - SCOPUS:85116412594
SN - 9783030876142
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 121
EP - 132
BT - Computational Diffusion MRI - 12th International Workshop, CDMRI 2021, Held in Conjunction with MICCAI 2021, Proceedings
A2 - Cetin-Karayumak, Suheyla
A2 - Christiaens, Daan
A2 - Figini, Matteo
A2 - Guevara, Pamela
A2 - Gyori, Noemi
A2 - Nath, Vishwesh
A2 - Pieciak, Tomasz
PB - Springer
T2 - 12th International Workshop on Computational Diffusion MRI, CDMRI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
Y2 - 1 October 2021 through 1 October 2021
ER -
