Paul A Yushkevich1, Robert S C Amaral2, Jean C Augustinack3, Andrew R Bender4, Jeffrey D Bernstein5, Marina Boccardi6, Martina Bocchetta7, Alison C Burggren8, Valerie A Carr9, M Mallar Chakravarty10, Gaël Chételat11, Ana M Daugherty12, Lila Davachi13, Song-Lin Ding14, Arne Ekstrom15, Mirjam I Geerlings16, Abdul Hassan17, Yushan Huang18, J Eugenio Iglesias19, Renaud La Joie11, Geoffrey A Kerchner5, Karen F LaRocque9, Laura A Libby17, Nikolai Malykhin20, Susanne G Mueller21, Rosanna K Olsen22, Daniela J Palombo23, Mansi B Parekh24, John B Pluta25, Alison R Preston26, Jens C Pruessner27, Charan Ranganath28, Naftali Raz12, Margaret L Schlichting29, Dorothee Schoemaker27, Sachi Singh30, Craig E L Stark31, Nanthia Suthana32, Alexa Tompary33, Marta M Turowski30, Koen Van Leemput34, Anthony D Wagner35, Lei Wang36, Julie L Winterburn2, Laura E M Wisse16, Michael A Yassa31, Michael M Zeineh24. 1. Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, USA. Electronic address: pauly2@upenn.edu. 2. Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Canada. 3. A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, USA. 4. Institute of Gerontology, Wayne State University, USA. 5. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA; Stanford Center for Memory Disorders, USA. 6. LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine), IRCCS Centro S. Giovanni di Dio Fatebenefratelli, Italy. 7. LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine), IRCCS Centro S. Giovanni di Dio Fatebenefratelli, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy. 8. Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, USA. 9. Department of Psychology, Stanford University, USA. 10. Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Canada; Department of Psychiatry, Department of Biomedical Engineering, McGill University, Canada. 11. INSERM U1077, Universitè de Caen Basse-Normandie, UMR-S1077, Ecole Pratique des Hautes Etudes, CHU de Caen, U1077, Caen, France. 12. Institute of Gerontology, Wayne State University, USA; Psychology Department, Wayne State University, USA. 13. Department of Psychology, New York University, USA; Center for Neural Science, New York University, USA. 14. Allen Institute for Brain Science, USA. 15. Center for Neuroscience, University of California, Davis, USA; Department of Psychology, University of California, Davis, USA. 16. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Netherlands. 17. Center for Neuroscience, University of California, Davis, USA. 18. Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada. 19. A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, USA; Basque Center on Cognition, Brain and Language (BCBL), Donostia-San Sebastian, Spain. 20. Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada; Centre for Neuroscience, University of Alberta, Edmonton, Alberta, Canada. 21. Department of Radiology, University of California, San Francisco, USA; Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center, USA. 22. Rotman Research Institute, Baycrest, Canada. 23. VA Boston Healthcare System, USA. 24. Department of Radiology, Stanford University, USA. 25. Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, USA; Department of Biostatistics, University of Pennsylvania, USA. 26. Department of Psychology, The University of Texas at Austin, USA; Center for Learning and Memory, The University of Texas at Austin, USA; Department of Neuroscience, The University of Texas at Austin, USA. 27. McGill Centre for Studies in Aging, Faculty of Medicine, McGill University, Canada; Department of Psychology, McGill University, Canada. 28. Department of Psychology, University of California, Davis, USA; Center for Neuroscience, University of California, Davis, USA. 29. Department of Psychology, The University of Texas at Austin, USA; Center for Learning and Memory, The University of Texas at Austin, USA. 30. Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, USA. 31. Department of Neurobiology and Behavior, University of California, Irvine, USA. 32. Department of Neurosurgery, University of California, Los Angeles, USA. 33. Department of Psychology, New York University, USA. 34. A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, USA; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark. 35. Department of Psychology, Stanford University, USA; Neurosciences Program, Stanford University, USA. 36. Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, USA; Department of Radiology, Northwestern University Feinberg School of Medicine, USA.
Abstract
OBJECTIVE: An increasing number of human in vivo magnetic resonance imaging (MRI) studies have focused on examining the structure and function of the subfields of the hippocampal formation (the dentate gyrus, CA fields 1-3, and the subiculum) and subregions of the parahippocampal gyrus (entorhinal, perirhinal, and parahippocampal cortices). The ability to interpret the results of such studies and to relate them to each other would be improved if a common standard existed for labeling hippocampal subfields and parahippocampal subregions. Currently, research groups label different subsets of structures and use different rules, landmarks, and cues to define their anatomical extents. This paper characterizes, both qualitatively and quantitatively, the variability in the existing manual segmentation protocols for labeling hippocampal and parahippocampal substructures in MRI, with the goal of guiding subsequent work on developing a harmonized substructure segmentation protocol. METHOD: MRI scans of a single healthy adult human subject were acquired both at 3 T and 7 T. Representatives from 21 research groups applied their respective manual segmentation protocols to the MRI modalities of their choice. The resulting set of 21 segmentations was analyzed in a common anatomical space to quantify similarity and identify areas of agreement. RESULTS: The differences between the 21 protocols include the region within which segmentation is performed, the set of anatomical labels used, and the extents of specific anatomical labels. The greatest overall disagreement among the protocols is at the CA1/subiculum boundary, and disagreement across all structures is greatest in the anterior portion of the hippocampal formation relative to the body and tail. CONCLUSIONS: The combined examination of the 21 protocols in the same dataset suggests possible strategies towards developing a harmonized subfield segmentation protocol and facilitates comparison between published studies.
OBJECTIVE: An increasing number of human in vivo magnetic resonance imaging (MRI) studies have focused on examining the structure and function of the subfields of the hippocampal formation (the dentate gyrus, CA fields 1-3, and the subiculum) and subregions of the parahippocampal gyrus (entorhinal, perirhinal, and parahippocampal cortices). The ability to interpret the results of such studies and to relate them to each other would be improved if a common standard existed for labeling hippocampal subfields and parahippocampal subregions. Currently, research groups label different subsets of structures and use different rules, landmarks, and cues to define their anatomical extents. This paper characterizes, both qualitatively and quantitatively, the variability in the existing manual segmentation protocols for labeling hippocampal and parahippocampal substructures in MRI, with the goal of guiding subsequent work on developing a harmonized substructure segmentation protocol. METHOD: MRI scans of a single healthy adult human subject were acquired both at 3 T and 7 T. Representatives from 21 research groups applied their respective manual segmentation protocols to the MRI modalities of their choice. The resulting set of 21 segmentations was analyzed in a common anatomical space to quantify similarity and identify areas of agreement. RESULTS: The differences between the 21 protocols include the region within which segmentation is performed, the set of anatomical labels used, and the extents of specific anatomical labels. The greatest overall disagreement among the protocols is at the CA1/subiculum boundary, and disagreement across all structures is greatest in the anterior portion of the hippocampal formation relative to the body and tail. CONCLUSIONS: The combined examination of the 21 protocols in the same dataset suggests possible strategies towards developing a harmonized subfield segmentation protocol and facilitates comparison between published studies.
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