Fully Automatic Hippocampus Segmentation and Classification in Alzheimer’s Disease and Mild Cognitive Impairment Applied on Data from ADNI

By Marie Chupin, Emilie Gerardin, Rémi Cuingnet, C. Boutet, L. Lemieux, Stéphane Lehéricy, Habib Benali, Line Garnero, Olivier Colliot. Hippocampus, 2009

361 citations recorded by the source corpus.

The hippocampus is among the first structures affected in Alzheimer's disease (AD). Hippocampal magnetic resonance imaging volumetry is a potential biomarker for AD but is hindered by the limitations of manual segmentation. We proposed a fully automatic method using probabilistic and anatomical priors for hippocampus segmentation. Probabilistic information is derived from 16 young controls and anatomical knowledge is modeled with automatically detected landmarks. The results were previously evaluated by comparison with manual segmentation on data from the 16 young healthy controls, with a leave‐one‐out strategy, and eight patients with AD. High accuracy was found for both groups (volume error 6 and 7%, overlap 87 and 86%, respectively). In this article, the method was used to segment 145 patients with AD, 294 patients with mild cognitive impairment (MCI), and 166 elderly normal subjects from the Alzheimer's Disease Neuroimaging Initiative database. On the basis of a qualitative rating protocol, the segmentation proved acceptable in 94% of the cases. We used the obtained hippocampal volumes to automatically discriminate between AD patients, MCI patients, and elderly controls. The classification proved accurate: 76% of the patients with AD and 71% of the MCI converting to AD before 18 months were correctly classified with respect to the elderly controls, using only hippocampal volume. © 2009 Wiley‐Liss, Inc.

Subfields: Neurology, Physiology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/a9598f6ba616acd3a9d0902d110193df25104526

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Neurology

1. Arthur W. Toga
2. John Q. Trojanowski
3. William J. Jagust
4. Ronald C. Petersen
5. Clifford R. Jack Jr.

In Physiology

1. Clifford R. Jack Jr.
2. William J. Jagust
3. John Q. Trojanowski
4. P. Aisen
5. Ronald C. Petersen
6. Olivier Colliot
7. Stéphane Lehéricy
8. Marie Chupin

In Psychiatry and Mental health

1. Clifford R. Jack Jr.
2. Ronald C. Petersen
3. William J. Jagust
4. John Q. Trojanowski
5. P. Aisen
6. Olivier Colliot
7. Stéphane Lehéricy
8. Marie Chupin

Citations in the corpus

• A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI (2015, neutral): “Despite its internal complexity, limits in MRI resolution have traditionally forced researchers to model the hippocampus as a single, homogeneous structure in neuroimaging studies of aging and AD (Boccardi et al., 2011; Chupin et al., 2009).”
• The Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception (2011, methodology): “Hippocampal volume 82 89 49 89 62 67 [68]**”
• A Bayesian model of shape and appearance for subcortical brain segmentation (2011, neutral): “, 2007, 2009); homotopic region deformation (Lehéricy et al., 2009); level-sets within a Bayesian framework (Cremers et al.”
• SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining (2021, neutral): “In this experiment, we evaluate SynthSeg in a proof-of-concept volumetric group study, where we assess its ability to detect hippocampal atrophy related to AD [15].”
• Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects (2014, neutral): “Over the past few years, numerous MRI biomarkers have been proposed in classifying AD patients in different disease stages (Fan et al., 2008; Duchesne et al., 2008; Chupin et al., 2009; Querbes et al., 2009; Wolz et al., 2011; Hinrichs et al., 2011; Westman et al., 2011a,b; Westman et al., 2012; Cho”
• Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database (2011, methodology): “…al., Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database, NeuroImage (2010) , doi:10.1016/j.neuroimage.2010.06.013 taking into consideration the quality control (Chupin et al., 2009b) of the hippocampus segmentation.”