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The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction.
Makropoulos, Antonios; Robinson, Emma C; Schuh, Andreas; Wright, Robert; Fitzgibbon, Sean; Bozek, Jelena; Counsell, Serena J; Steinweg, Johannes; Vecchiato, Katy; Passerat-Palmbach, Jonathan; Lenz, Gregor; Mortari, Filippo; Tenev, Tencho; Duff, Eugene P; Bastiani, Matteo; Cordero-Grande, Lucilio; Hughes, Emer; Tusor, Nora; Tournier, Jacques-Donald; Hutter, Jana; Price, Anthony N; Teixeira, Rui Pedro A G; Murgasova, Maria; Victor, Suresh; Kelly, Christopher; Rutherford, Mary A; Smith, Stephen M; Edwards, A David; Hajnal, Joseph V; Jenkinson, Mark; Rueckert, Daniel.
Afiliação
  • Makropoulos A; Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
  • Robinson EC; Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom. Electronic address: emma.robinson@kcl.ac.uk.
  • Schuh A; Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
  • Wright R; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Fitzgibbon S; Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
  • Bozek J; Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia.
  • Counsell SJ; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Steinweg J; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Vecchiato K; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Passerat-Palmbach J; Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
  • Lenz G; Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
  • Mortari F; Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
  • Tenev T; Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
  • Duff EP; Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
  • Bastiani M; Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
  • Cordero-Grande L; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Hughes E; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Tusor N; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Tournier JD; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Hutter J; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Price AN; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Teixeira RPAG; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Murgasova M; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Victor S; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Kelly C; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Rutherford MA; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Smith SM; Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
  • Edwards AD; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Hajnal JV; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
  • Jenkinson M; Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
  • Rueckert D; Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
Neuroimage ; 173: 88-112, 2018 06.
Article em En | MEDLINE | ID: mdl-29409960
ABSTRACT
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Encéfalo / Conectoma Tipo de estudo: Guideline Limite: Female / Humans / Male / Newborn Idioma: En Revista: Neuroimage Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Encéfalo / Conectoma Tipo de estudo: Guideline Limite: Female / Humans / Male / Newborn Idioma: En Revista: Neuroimage Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Reino Unido