Literature DB >> 29966720

The EADC-ADNI harmonized protocol for hippocampal segmentation: A validation study.

Azar Zandifar1, Vladimir S Fonov2, Jens C Pruessner3, D Louis Collins4.   

Abstract

Recently, a group of major international experts have completed a comprehensive effort to efficiently define a harmonized protocol for manual hippocampal segmentation that is optimized for Alzheimer's research (known as the EADC-ADNI Harmonized Protocol (the HarP)). This study compares the HarP with one of the widely used hippocampal segmentation protocols (Pruessner, 2000), based on a single automatic segmentation method trained separately with libraries made from each manual segmentation protocol. The automatic segmentation conformity with the corresponding manual segmentation and the ability to capture Alzheimer's disease related hippocampal atrophy on large datasets are measured to compare the manual protocols. In addition to the possibility of harmonizing different procedures of hippocampal segmentation, our results show that using the HarP, the automatic segmentation conformity with manual segmentation is also preserved (Dice's κ=0.88,κ=0.87 for Pruessner and HarP respectively (p = 0.726 for common training library)). Furthermore, the results show that the HarP can capture the Alzheimer's disease related hippocampal volume differences in large datasets. The HarP-derived segmentation shows large effect size (Cohen's d = 1.5883) in separating Alzheimer's Disease patients versus normal controls (AD:NC) and medium effect size (Cohen's d = 0.5747) in separating stable versus progressive Mild Cognitively Impaired patients (sMCI:pMCI). Furthermore, the area under the ROC curve for a LDA classifier trained based on age, sex and HarP-derived hippocampal volume is 0.8858 for AD:NC, and for 0.6677 sMCI:pMCI. These results show that the harmonized protocol-derived labels can be widely used in clinic and research, as a sensitive and accurate way of delineating the hippocampus.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Alzheimer's disease; Area under receiver operating characteristic curve (AUC); Cohen's d; Dice's κ; EADC-ADNI harmonized protocol (the HarP); Hippocampal segmentation; Pruessner protocol

Mesh:

Year:  2018        PMID: 29966720     DOI: 10.1016/j.neuroimage.2018.06.077

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  2 in total

1.  Integrated 3d flow-based multi-atlas brain structure segmentation.

Authors:  Yeshu Li; Ziming Qiu; Xingyu Fan; Xianglong Liu; Eric I-Chao Chang; Yan Xu
Journal:  PLoS One       Date:  2022-08-15       Impact factor: 3.752

2.  Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets.

Authors:  Valentina Bordin; Ilaria Bertani; Irene Mattioli; Vaanathi Sundaresan; Paul McCarthy; Sana Suri; Enikő Zsoldos; Nicola Filippini; Abda Mahmood; Luca Melazzini; Maria Marcella Laganà; Giovanna Zamboni; Archana Singh-Manoux; Mika Kivimäki; Klaus P Ebmeier; Giuseppe Baselli; Mark Jenkinson; Clare E Mackay; Eugene P Duff; Ludovica Griffanti
Journal:  Neuroimage       Date:  2021-05-20       Impact factor: 6.556

  2 in total

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