Operationalizing protocol differences for EADC-ADNI manual hippocampal segmentation

Abstract Background Hippocampal volumetry on magnetic resonance imaging is recognized as an Alzheimer's disease (AD) biomarker, and manual segmentation is the gold standard for measurement. However, a standard procedure is lacking. We operationalize and quantitate landmark differences to help a...

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Published in:Alzheimer's & dementia Vol. 11; no. 2; pp. 184 - 194
Main Authors: Boccardi, Marina, Bocchetta, Martina, Ganzola, Rossana, Robitaille, Nicolas, Redolfi, Alberto, Duchesne, Simon, Jack, Clifford R, Frisoni, Giovanni B
Format: Journal Article
Language:English
Published: United States Elsevier Inc 01-02-2015
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Summary:Abstract Background Hippocampal volumetry on magnetic resonance imaging is recognized as an Alzheimer's disease (AD) biomarker, and manual segmentation is the gold standard for measurement. However, a standard procedure is lacking. We operationalize and quantitate landmark differences to help a Delphi panel converge on a set of landmarks. Methods One hundred percent of anatomic landmark variability across 12 different protocols for manual segmentation was reduced into four segmentation units (the minimum hippocampus, the alveus/fimbria, the tail, and the subiculum), which were segmented on magnetic resonance images by expert raters to estimate reliability and AD-related atrophy. Results Intra- and interrater reliability were more than 0.96 and 0.92, respectively, except for the alveus/fimbria, which were 0.86 and 0.77, respectively. Of all AD-related atrophy, the minimum hippocampus contributed to 67%; tail, 24%; alveus/fimbria, 4%; and the subiculum, 5%. Conclusions Anatomic landmark variability in available protocols can be reduced to four discrete and measurable segmentation units. Their quantitative assessment will help a Delphi panel to define a set of landmarks for a harmonized protocol.
Bibliography:As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data, but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at
Project collaborators include: George Bartzokis (Department of Psychiatry, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA), John G. Csernansky (Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA), Mony J. de Leon (New York University School of Medicine, Center for Brain Health, New York, NY, USA), Leyla deToledo‐Morrell (Department of Neurological Sciences, Rush University, Chicago, IL, USA), Ronald J. Killiany (Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA), Stéphane Lehéricy (Center for NeuroImaging Research and Department of Neuroradiology, Université Pierre et Marie Curie‐Paris 6, Groupe Hospitalier Pitié‐Salpêtrière, Paris, France), Nikolai Malykhin (Department of Biomedical Engineering, Centre for Neuroscience, University of Alberta, Edmonton, Alberta, Canada), Johannes Pantel (Institute of General Practice, University of Frankfurt/Main, Germany), Jens C. Pruessner (McGill Centre for Studies in Aging, Department of Psychiatry, McGill University, Montreal, Quebec, Canada), Hilkka Soininen (Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland), and Craig Watson (Wayne State University School of Medicine, University Health Center, St. Antoine, Detroit, MI, USA).
http://adni.loni.ucla.edu/wp‐content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database
This manuscript was approved by the ADNI Data and Publication Committee on June 4, 2012.
www.loni.ucla.edu/ADNI
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ISSN:1552-5260
1552-5279
DOI:10.1016/j.jalz.2013.03.001