Surface Agnostic Metrics for Cortical Volume Segmentation and Regression

Samuel Budd; Prachi Patkee; Ana Baburamani; Mary Rutherford; Emma C. Robinson; Bernhard Kainz

doi:10.1007/978-3-030-66843-3_1

Surface Agnostic Metrics for Cortical Volume Segmentation and Regression

Samuel Budd^*, Prachi Patkee, Ana Baburamani, Mary Rutherford, Emma C. Robinson, Bernhard Kainz

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders. Current analysis of cortical variation is typically performed by fitting surface mesh models to inner and outer cortical boundaries and investigating metrics such as surface area and cortical curvature or thickness. These, however, take a long time to run, and are sensitive to motion and image and surface resolution, which can prohibit their use in clinical settings. In this paper, we instead propose a machine learning solution, training a novel architecture to predict cortical thickness and curvature metrics from T2 MRI images, while additionally returning metrics of prediction uncertainty. Our proposed model is tested on a clinical cohort (Down Syndrome) for which surface-based modelling often fails. Results suggest that deep convolutional neural networks are a viable option to predict cortical metrics across a range of brain development stages and pathologies.

Original language	English
Title of host publication	Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - 3rd International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Proceedings
Editors	Seyed Mostafa Kia, Hassan Mohy-ud-Din, Ahmed Abdulkadir, Cher Bass, Mohamad Habes, Jane Maryam Rondina, Chantal Tax, Hongzhi Wang, Thomas Wolfers, Saima Rathore, Madhura Ingalhalikar
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	3-12
Number of pages	10
ISBN (Print)	9783030668426
DOIs	https://doi.org/10.1007/978-3-030-66843-3_1
Publication status	Published - 2020
Event	3rd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and 2nd International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020 - Lima, Peru Duration: 4 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	12449 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and 2nd International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020
Country/Territory	Peru
City	Lima
Period	4/10/2020 → 8/10/2020

Access to Document

10.1007/978-3-030-66843-3_1

Cite this

Budd, S., Patkee, P., Baburamani, A., Rutherford, M., Robinson, E. C., & Kainz, B. (2020). Surface Agnostic Metrics for Cortical Volume Segmentation and Regression. In S. M. Kia, H. Mohy-ud-Din, A. Abdulkadir, C. Bass, M. Habes, J. M. Rondina, C. Tax, H. Wang, T. Wolfers, S. Rathore, & M. Ingalhalikar (Eds.), Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - 3rd International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Proceedings (pp. 3-12). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12449 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-66843-3_1

Budd, Samuel ; Patkee, Prachi ; Baburamani, Ana et al. / Surface Agnostic Metrics for Cortical Volume Segmentation and Regression. Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - 3rd International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Proceedings. editor / Seyed Mostafa Kia ; Hassan Mohy-ud-Din ; Ahmed Abdulkadir ; Cher Bass ; Mohamad Habes ; Jane Maryam Rondina ; Chantal Tax ; Hongzhi Wang ; Thomas Wolfers ; Saima Rathore ; Madhura Ingalhalikar. Springer Science and Business Media Deutschland GmbH, 2020. pp. 3-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Surface Agnostic Metrics for Cortical Volume Segmentation and Regression",

abstract = "The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders. Current analysis of cortical variation is typically performed by fitting surface mesh models to inner and outer cortical boundaries and investigating metrics such as surface area and cortical curvature or thickness. These, however, take a long time to run, and are sensitive to motion and image and surface resolution, which can prohibit their use in clinical settings. In this paper, we instead propose a machine learning solution, training a novel architecture to predict cortical thickness and curvature metrics from T2 MRI images, while additionally returning metrics of prediction uncertainty. Our proposed model is tested on a clinical cohort (Down Syndrome) for which surface-based modelling often fails. Results suggest that deep convolutional neural networks are a viable option to predict cortical metrics across a range of brain development stages and pathologies.",

author = "Samuel Budd and Prachi Patkee and Ana Baburamani and Mary Rutherford and Robinson, {Emma C.} and Bernhard Kainz",

note = "Funding Information: We thank the parents and children who participated in this study. This work was supported by the Medical Research Council [MR/K006355/1]; Rosetrees Trust [A1563], Fondation J?r?me Lejeune [2017b ? 1707], Sparks and Great Ormond Street Hospital Children?s Charity [V5318]. The research leading to these results has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP/2007?2013)/ERC Grant Agreement no. 319456. The work of E.C.R. was supported by the Academy of Medical Sci-ences/the British Heart Foundation/the Government Department of Business, Energy and Industrial Strategy/the Wellcome Trust Springboard Award [SBF003/1116]. We also gratefully acknowledge financial support from the Wellcome Trust IEH 102431, EPSRC (EP/S022104/1, EP/S013687/1), EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z], the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) based at Guy?s and St Thomas? NHS Foundation Trust and King?s College London and supported by the NIHR Clinical Research Facility (CRF) at Guy?s and St Thomas?, and Nvidia GPU donations. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 3rd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and 2nd International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",

year = "2020",

doi = "10.1007/978-3-030-66843-3_1",

language = "English",

isbn = "9783030668426",

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 = "3--12",

editor = "Kia, {Seyed Mostafa} and Hassan Mohy-ud-Din and Ahmed Abdulkadir and Cher Bass and Mohamad Habes and Rondina, {Jane Maryam} and Chantal Tax and Hongzhi Wang and Thomas Wolfers and Saima Rathore and Madhura Ingalhalikar",

booktitle = "Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - 3rd International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Proceedings",

address = "Germany",

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Budd, S, Patkee, P , Baburamani, A , Rutherford, M , Robinson, EC & Kainz, B 2020, Surface Agnostic Metrics for Cortical Volume Segmentation and Regression. in SM Kia, H Mohy-ud-Din, A Abdulkadir, C Bass, M Habes, JM Rondina, C Tax, H Wang, T Wolfers, S Rathore & M Ingalhalikar (eds), Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - 3rd International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 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. 12449 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 3-12, 3rd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and 2nd International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, Lima, Peru, 4/10/2020. https://doi.org/10.1007/978-3-030-66843-3_1

Surface Agnostic Metrics for Cortical Volume Segmentation and Regression. / Budd, Samuel; Patkee, Prachi ; Baburamani, Ana et al.
Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - 3rd International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Proceedings. ed. / Seyed Mostafa Kia; Hassan Mohy-ud-Din; Ahmed Abdulkadir; Cher Bass; Mohamad Habes; Jane Maryam Rondina; Chantal Tax; Hongzhi Wang; Thomas Wolfers; Saima Rathore; Madhura Ingalhalikar. Springer Science and Business Media Deutschland GmbH, 2020. p. 3-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12449 LNCS).

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

TY - CHAP

T1 - Surface Agnostic Metrics for Cortical Volume Segmentation and Regression

AU - Budd, Samuel

AU - Patkee, Prachi

AU - Baburamani, Ana

AU - Rutherford, Mary

AU - Robinson, Emma C.

AU - Kainz, Bernhard

N1 - Funding Information: We thank the parents and children who participated in this study. This work was supported by the Medical Research Council [MR/K006355/1]; Rosetrees Trust [A1563], Fondation J?r?me Lejeune [2017b ? 1707], Sparks and Great Ormond Street Hospital Children?s Charity [V5318]. The research leading to these results has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP/2007?2013)/ERC Grant Agreement no. 319456. The work of E.C.R. was supported by the Academy of Medical Sci-ences/the British Heart Foundation/the Government Department of Business, Energy and Industrial Strategy/the Wellcome Trust Springboard Award [SBF003/1116]. We also gratefully acknowledge financial support from the Wellcome Trust IEH 102431, EPSRC (EP/S022104/1, EP/S013687/1), EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z], the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) based at Guy?s and St Thomas? NHS Foundation Trust and King?s College London and supported by the NIHR Clinical Research Facility (CRF) at Guy?s and St Thomas?, and Nvidia GPU donations. Publisher Copyright: © 2020, Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders. Current analysis of cortical variation is typically performed by fitting surface mesh models to inner and outer cortical boundaries and investigating metrics such as surface area and cortical curvature or thickness. These, however, take a long time to run, and are sensitive to motion and image and surface resolution, which can prohibit their use in clinical settings. In this paper, we instead propose a machine learning solution, training a novel architecture to predict cortical thickness and curvature metrics from T2 MRI images, while additionally returning metrics of prediction uncertainty. Our proposed model is tested on a clinical cohort (Down Syndrome) for which surface-based modelling often fails. Results suggest that deep convolutional neural networks are a viable option to predict cortical metrics across a range of brain development stages and pathologies.

AB - The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders. Current analysis of cortical variation is typically performed by fitting surface mesh models to inner and outer cortical boundaries and investigating metrics such as surface area and cortical curvature or thickness. These, however, take a long time to run, and are sensitive to motion and image and surface resolution, which can prohibit their use in clinical settings. In this paper, we instead propose a machine learning solution, training a novel architecture to predict cortical thickness and curvature metrics from T2 MRI images, while additionally returning metrics of prediction uncertainty. Our proposed model is tested on a clinical cohort (Down Syndrome) for which surface-based modelling often fails. Results suggest that deep convolutional neural networks are a viable option to predict cortical metrics across a range of brain development stages and pathologies.

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U2 - 10.1007/978-3-030-66843-3_1

DO - 10.1007/978-3-030-66843-3_1

M3 - Conference paper

AN - SCOPUS:85101542597

SN - 9783030668426

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

SP - 3

EP - 12

BT - Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - 3rd International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Proceedings

A2 - Kia, Seyed Mostafa

A2 - Mohy-ud-Din, Hassan

A2 - Abdulkadir, Ahmed

A2 - Bass, Cher

A2 - Habes, Mohamad

A2 - Rondina, Jane Maryam

A2 - Tax, Chantal

A2 - Wang, Hongzhi

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A2 - Ingalhalikar, Madhura

PB - Springer Science and Business Media Deutschland GmbH

T2 - 3rd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and 2nd International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020

Y2 - 4 October 2020 through 8 October 2020

ER -

Budd S, Patkee P , Baburamani A , Rutherford M , Robinson EC , Kainz B. Surface Agnostic Metrics for Cortical Volume Segmentation and Regression. In Kia SM, Mohy-ud-Din H, Abdulkadir A, Bass C, Habes M, Rondina JM, Tax C, Wang H, Wolfers T, Rathore S, Ingalhalikar M, editors, Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - 3rd International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 3-12. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-66843-3_1

Surface Agnostic Metrics for Cortical Volume Segmentation and Regression

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