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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	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
Editors	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
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	174-186
Number of pages	13
ISBN (Print)	9783030603649
DOIs	https://doi.org/10.1007/978-3-030-60365-6_17
Publication status	Published - 1 Jan 2020
Externally published	Yes
Event	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, Peru Duration: 8 Oct 2020 → 8 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12443 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	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
Country/Territory	Peru
City	Lima
Period	8/10/20 → 8/10/20

Keywords

Brain age
Cortical surface
Developing brain
Geometric deep learning
Graph neural networks
MeshCNN
PointNet

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

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

Cite this

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. In 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 (pp. 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. editor / 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. pp. 174-186 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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",

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

language = "English",

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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",

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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",

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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. in 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, pp. 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, Peru, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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

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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.

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KW - Cortical surface

KW - Developing brain

KW - Geometric deep learning

KW - Graph neural networks

KW - MeshCNN

KW - PointNet

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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)

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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

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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. In 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, editors, 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

Abstract

Publication series

Conference

Keywords

UN SDGs

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Cite this