Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface

Vitalis Vosylius; Andy Wang; Cemlyn Waters; Alexey Zakharov; Francis Ward; Loic Le Folgoc; John Cupitt; Antonios Makropoulos; Andreas Schuh; Daniel Rueckert; Amir Alansary

doi:10.1007/978-3-030-60365-6_17

Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface

Vitalis Vosylius
, Andy Wang
, Cemlyn Waters
, Alexey Zakharov
, Francis Ward
, Loic Le Folgoc
, John Cupitt
, Antonios Makropoulos
, Andreas Schuh
, Daniel Rueckert
, Amir Alansary

Imperial College London

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

Accurate estimation of the age in neonates is useful for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface. We utilize and compare multiple specialized neural network architectures that predict the age using different geometric representations of the cortical surface; we compare MeshCNN, Pointnet++, GraphCNN, and a volumetric benchmark. The dataset is part of the Developing Human Connectome Project (dHCP), and is a cohort of healthy and premature neonates. We evaluate our approach on 650 subjects (727 scans) with PA ranging from 27 to 45 weeks. Our results show accurate prediction of the estimated PA, with mean error less than one week.

langue originale	Anglais
titre	Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis - 2nd International Workshop, UNSURE 2020, and 3rd International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Proceedings
rédacteurs en chef	Carole H. Sudre, Hamid Fehri, Tal Arbel, Christian F. Baumgartner, Adrian Dalca, Ryutaro Tanno, Koen Van Leemput, William M. Wells, Aristeidis Sotiras, Bartlomiej Papiez, Enzo Ferrante, Sarah Parisot
Editeur	Springer Science and Business Media Deutschland GmbH
Pages	174-186
Nombre de pages	13
ISBN (imprimé)	9783030603649
Les DOIs	https://doi.org/10.1007/978-3-030-60365-6_17
état	Publié - 1 janv. 2020
Modification externe	Oui
Evénement	2nd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the 3rd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Pérou Durée: 8 oct. 2020 → 8 oct. 2020

Série de publications

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12443 LNCS
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	2nd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the 3rd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Pays/Territoire	Pérou
La ville	Lima
période	8/10/20 → 8/10/20

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 3 Bonne santé et bien-être

Accès au document

10.1007/978-3-030-60365-6_17

Contient cette citation

Vosylius, V., Wang, A., Waters, C., Zakharov, A., Ward, F., Le Folgoc, L., Cupitt, J., Makropoulos, A., Schuh, A., Rueckert, D., & Alansary, A. (2020). Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface. Dans C. H. Sudre, H. Fehri, T. Arbel, C. F. Baumgartner, A. Dalca, R. Tanno, K. Van Leemput, W. M. Wells, A. Sotiras, B. Papiez, E. Ferrante, & S. Parisot (eds.), Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis - 2nd International Workshop, UNSURE 2020, and 3rd International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Proceedings (p. 174-186). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 12443 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60365-6_17

Vosylius, Vitalis ; Wang, Andy ; Waters, Cemlyn et al. / Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis - 2nd International Workshop, UNSURE 2020, and 3rd International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Proceedings. Editeur / Carole H. Sudre ; Hamid Fehri ; Tal Arbel ; Christian F. Baumgartner ; Adrian Dalca ; Ryutaro Tanno ; Koen Van Leemput ; William M. Wells ; Aristeidis Sotiras ; Bartlomiej Papiez ; Enzo Ferrante ; Sarah Parisot. Springer Science and Business Media Deutschland GmbH, 2020. p. 174-186 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e050ec51bb0c41c0a1b048c7d3530a99,

title = "Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface",

abstract = "Accurate estimation of the age in neonates is useful for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface. We utilize and compare multiple specialized neural network architectures that predict the age using different geometric representations of the cortical surface; we compare MeshCNN, Pointnet++, GraphCNN, and a volumetric benchmark. The dataset is part of the Developing Human Connectome Project (dHCP), and is a cohort of healthy and premature neonates. We evaluate our approach on 650 subjects (727 scans) with PA ranging from 27 to 45 weeks. Our results show accurate prediction of the estimated PA, with mean error less than one week.",

keywords = "Brain age, Cortical surface, Developing brain, Geometric deep learning, Graph neural networks, MeshCNN, PointNet",

author = "Vitalis Vosylius and Andy Wang and Cemlyn Waters and Alexey Zakharov and Francis Ward and \{Le Folgoc\}, Loic and John Cupitt and Antonios Makropoulos and Andreas Schuh and Daniel Rueckert and Amir Alansary",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 2nd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the 3rd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 08-10-2020 Through 08-10-2020",

year = "2020",

month = jan,

day = "1",

doi = "10.1007/978-3-030-60365-6\_17",

language = "English",

isbn = "9783030603649",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "174--186",

editor = "Sudre, \{Carole H.\} and Hamid Fehri and Tal Arbel and Baumgartner, \{Christian F.\} and Adrian Dalca and Ryutaro Tanno and \{Van Leemput\}, Koen and Wells, \{William M.\} and Aristeidis Sotiras and Bartlomiej Papiez and Enzo Ferrante and Sarah Parisot",

booktitle = "Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis - 2nd International Workshop, UNSURE 2020, and 3rd International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Proceedings",

}

Vosylius, V, Wang, A, Waters, C, Zakharov, A, Ward, F, Le Folgoc, L, Cupitt, J, Makropoulos, A, Schuh, A, Rueckert, D & Alansary, A 2020, Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface. Dans CH Sudre, H Fehri, T Arbel, CF Baumgartner, A Dalca, R Tanno, K Van Leemput, WM Wells, A Sotiras, B Papiez, E Ferrante & S Parisot (eds), Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis - 2nd International Workshop, UNSURE 2020, and 3rd International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), VOL. 12443 LNCS, Springer Science and Business Media Deutschland GmbH, p. 174-186, 2nd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the 3rd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, Pérou, 8/10/20. https://doi.org/10.1007/978-3-030-60365-6_17

Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface. / Vosylius, Vitalis; Wang, Andy; Waters, Cemlyn et al.
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis - 2nd International Workshop, UNSURE 2020, and 3rd International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Proceedings. Ed. / Carole H. Sudre; Hamid Fehri; Tal Arbel; Christian F. Baumgartner; Adrian Dalca; Ryutaro Tanno; Koen Van Leemput; William M. Wells; Aristeidis Sotiras; Bartlomiej Papiez; Enzo Ferrante; Sarah Parisot. Springer Science and Business Media Deutschland GmbH, 2020. p. 174-186 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 12443 LNCS).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface

AU - Vosylius, Vitalis

AU - Wang, Andy

AU - Waters, Cemlyn

AU - Zakharov, Alexey

AU - Ward, Francis

AU - Le Folgoc, Loic

AU - Cupitt, John

AU - Makropoulos, Antonios

AU - Schuh, Andreas

AU - Rueckert, Daniel

AU - Alansary, Amir

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Accurate estimation of the age in neonates is useful for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface. We utilize and compare multiple specialized neural network architectures that predict the age using different geometric representations of the cortical surface; we compare MeshCNN, Pointnet++, GraphCNN, and a volumetric benchmark. The dataset is part of the Developing Human Connectome Project (dHCP), and is a cohort of healthy and premature neonates. We evaluate our approach on 650 subjects (727 scans) with PA ranging from 27 to 45 weeks. Our results show accurate prediction of the estimated PA, with mean error less than one week.

AB - Accurate estimation of the age in neonates is useful for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface. We utilize and compare multiple specialized neural network architectures that predict the age using different geometric representations of the cortical surface; we compare MeshCNN, Pointnet++, GraphCNN, and a volumetric benchmark. The dataset is part of the Developing Human Connectome Project (dHCP), and is a cohort of healthy and premature neonates. We evaluate our approach on 650 subjects (727 scans) with PA ranging from 27 to 45 weeks. Our results show accurate prediction of the estimated PA, with mean error less than one week.

KW - Brain age

KW - Cortical surface

KW - Developing brain

KW - Geometric deep learning

KW - Graph neural networks

KW - MeshCNN

KW - PointNet

U2 - 10.1007/978-3-030-60365-6_17

DO - 10.1007/978-3-030-60365-6_17

M3 - Conference contribution

AN - SCOPUS:85093079195

SN - 9783030603649

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

SP - 174

EP - 186

BT - Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis - 2nd International Workshop, UNSURE 2020, and 3rd International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Proceedings

A2 - Sudre, Carole H.

A2 - Fehri, Hamid

A2 - Arbel, Tal

A2 - Baumgartner, Christian F.

A2 - Dalca, Adrian

A2 - Tanno, Ryutaro

A2 - Van Leemput, Koen

A2 - Wells, William M.

A2 - Sotiras, Aristeidis

A2 - Papiez, Bartlomiej

A2 - Ferrante, Enzo

A2 - Parisot, Sarah

PB - Springer Science and Business Media Deutschland GmbH

T2 - 2nd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the 3rd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

Y2 - 8 October 2020 through 8 October 2020

ER -

Vosylius V, Wang A, Waters C, Zakharov A, Ward F, Le Folgoc L et al. Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface. Dans Sudre CH, Fehri H, Arbel T, Baumgartner CF, Dalca A, Tanno R, Van Leemput K, Wells WM, Sotiras A, Papiez B, Ferrante E, Parisot S, rédacteurs en chef, Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis - 2nd International Workshop, UNSURE 2020, and 3rd International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 174-186. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-60365-6_17

Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface

Résumé

Série de publications

Une conférence

SDG des Nations Unies

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