Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images
The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases in...
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| Vydáno v: | NeuroImage (Orlando, Fla.) Ročník 183; s. 150 - 172 |
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| Hlavní autoři: | , , , , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Elsevier Inc
01.12.2018
Elsevier Limited Elsevier |
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| ISSN: | 1053-8119, 1095-9572, 1095-9572 |
| On-line přístup: | Získat plný text |
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| Abstract | The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.
•First paper to evaluate the state-of-the-art in cerebellum parcellation.•Presenting results on both Adult and Pediatric Cohorts.•Adult Cohort contains healthy controls, and patients with either symptoms of cerebellar dysfunction or SCA 6.•Pediatric Cohort contains healthy controls, and patients with ADHD or Autism. |
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| AbstractList | The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method. The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method. •First paper to evaluate the state-of-the-art in cerebellum parcellation.•Presenting results on both Adult and Pediatric Cohorts.•Adult Cohort contains healthy controls, and patients with either symptoms of cerebellar dysfunction or SCA 6.•Pediatric Cohort contains healthy controls, and patients with ADHD or Autism. The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method. |
| Author | Collins, D. Louis Dolz, Jose Cuzzocreo, Jennifer L. Han, Shuo Desrosiers, Christian Romero, José E. Carass, Aaron Mostofsky, Stewart H. Landman, Bennett A. Ganz, Melanie Beliveau, Vincent Ying, Sarah H. Crocetti, Deana Hernandez-Castillo, Carlos R. Ben Ayed, Ismail Thyreau, Benjamin Manjón, José V. Fonov, Vladimir S. Rasser, Paul E. Coupé, Pierrick Thompson, Paul M. Prince, Jerry L. Onyike, Chiadi U. |
| AuthorAffiliation | a Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA t Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA u Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA k Laboratory for Imagery, Vision, and Artificial Intelligence, École de Technologie Supérieure, Montreal, QC, Canada e Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 20892, USA m Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, España f Consejo Nacional de Ciencia y Tecnología, Instituto de Neuroetología, Universidad Veracruzana, Xalapa, Mexico s Center for Neurodevelopmental Medicine and Imaging Research, Kennedy Krieger Institut |
| AuthorAffiliation_xml | – name: h Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark – name: m Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, España – name: q Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA – name: a Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA – name: e Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 20892, USA – name: k Laboratory for Imagery, Vision, and Artificial Intelligence, École de Technologie Supérieure, Montreal, QC, Canada – name: l Institute of Development, Aging and Cancer, Tohoku University, Japan – name: t Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA – name: u Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA – name: c Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA – name: v Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, University of Southern California, Los Angeles, CA 90033, USA – name: i Department of Computer Science, University of Copenhagen, Copenhagen, Denmark – name: b Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA – name: n University of Bordeaux, LaBRI, UMR 5800, PICTURA, Talence, F-33400, France – name: r Department of Psychiatry and Behavioral Sciences, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA – name: p Image Processing Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada – name: s Center for Neurodevelopmental Medicine and Imaging Research, Kennedy Krieger Institute, Baltimore, MD 21205, USA – name: g Priority Research Centre for Brain & Mental Health and Stroke & Brain Injury, University of Newcastle, Callaghan NSW, Australia – name: d Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA – name: o CNRS, LaBRI, UMR 5800, PICTURA, Talence, F-33400, France – name: f Consejo Nacional de Ciencia y Tecnología, Instituto de Neuroetología, Universidad Veracruzana, Xalapa, Mexico – name: j Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark |
| Author_xml | – sequence: 1 givenname: Aaron surname: Carass fullname: Carass, Aaron email: aaron_carass@jhu.edu organization: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA – sequence: 2 givenname: Jennifer L. surname: Cuzzocreo fullname: Cuzzocreo, Jennifer L. organization: Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA – sequence: 3 givenname: Shuo surname: Han fullname: Han, Shuo organization: Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA – sequence: 4 givenname: Carlos R. surname: Hernandez-Castillo fullname: Hernandez-Castillo, Carlos R. organization: Consejo Nacional de Ciencia y Tecnología, Instituto de Neuroetología, Universidad Veracruzana, Xalapa, Mexico – sequence: 5 givenname: Paul E. surname: Rasser fullname: Rasser, Paul E. organization: Priority Research Centre for Brain & Mental Health and Stroke & Brain Injury, University of Newcastle, Callaghan, NSW, Australia – sequence: 6 givenname: Melanie surname: Ganz fullname: Ganz, Melanie organization: Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark – sequence: 7 givenname: Vincent surname: Beliveau fullname: Beliveau, Vincent organization: Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark – sequence: 8 givenname: Jose surname: Dolz fullname: Dolz, Jose organization: Laboratory for Imagery, Vision, and Artificial Intelligence, École de Technologie Supérieure, Montreal, QC, Canada – sequence: 9 givenname: Ismail surname: Ben Ayed fullname: Ben Ayed, Ismail organization: Laboratory for Imagery, Vision, and Artificial Intelligence, École de Technologie Supérieure, Montreal, QC, Canada – sequence: 10 givenname: Christian surname: Desrosiers fullname: Desrosiers, Christian organization: Laboratory for Imagery, Vision, and Artificial Intelligence, École de Technologie Supérieure, Montreal, QC, Canada – sequence: 11 givenname: Benjamin surname: Thyreau fullname: Thyreau, Benjamin organization: Institute of Development, Aging and Cancer, Tohoku University, Japan – sequence: 12 givenname: José E. surname: Romero fullname: Romero, José E. organization: Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain – sequence: 13 givenname: Pierrick surname: Coupé fullname: Coupé, Pierrick organization: University of Bordeaux, LaBRI, UMR 5800, PICTURA, Talence, F-33400, France – sequence: 14 givenname: José V. surname: Manjón fullname: Manjón, José V. organization: Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain – sequence: 15 givenname: Vladimir S. surname: Fonov fullname: Fonov, Vladimir S. organization: Image Processing Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada – sequence: 16 givenname: D. Louis surname: Collins fullname: Collins, D. Louis organization: Image Processing Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada – sequence: 17 givenname: Sarah H. surname: Ying fullname: Ying, Sarah H. organization: Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA – sequence: 18 givenname: Chiadi U. surname: Onyike fullname: Onyike, Chiadi U. organization: Department of Psychiatry and Behavioral Sciences, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA – sequence: 19 givenname: Deana surname: Crocetti fullname: Crocetti, Deana organization: Center for Neurodevelopmental Medicine and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205, USA – sequence: 20 givenname: Bennett A. surname: Landman fullname: Landman, Bennett A. organization: Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, 37235, USA – sequence: 21 givenname: Stewart H. surname: Mostofsky fullname: Mostofsky, Stewart H. organization: Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA – sequence: 22 givenname: Paul M. surname: Thompson fullname: Thompson, Paul M. organization: Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, 90292, USA – sequence: 23 givenname: Jerry L. surname: Prince fullname: Prince, Jerry L. organization: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30099076$$D View this record in MEDLINE/PubMed https://hal.science/hal-01918431$$DView record in HAL |
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| ContentType | Journal Article |
| Copyright | 2018 Elsevier Inc. Copyright © 2018 Elsevier Inc. All rights reserved. 2018. Elsevier Inc. Distributed under a Creative Commons Attribution 4.0 International License |
| Copyright_xml | – notice: 2018 Elsevier Inc. – notice: Copyright © 2018 Elsevier Inc. All rights reserved. – notice: 2018. Elsevier Inc. – notice: Distributed under a Creative Commons Attribution 4.0 International License |
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| DOI | 10.1016/j.neuroimage.2018.08.003 |
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| Keywords | Autism Magnetic resonance imaging Cerebellar ataxia Attention deficit hyperactivity disorder |
| Language | English |
| License | Copyright © 2018 Elsevier Inc. All rights reserved. Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 |
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| Title | Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images |
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