Nicolas Robitaille

Delphi definition of the EADC-ADNI harmonized protocol for hippocampal segmentation on magnetic resonance

This study aimed to have international experts converge on a harmonized definition of whole hippocampus boundaries and segmentation procedures, to define standard operating procedures for magnetic resonance (MR)‐based manual hippocampal segmentation.

Operationalizing protocol differences for EADC-ADNI manual hippocampal segmentation

Hippocampal volumetry on magnetic resonance imaging is recognized as an Alzheimers 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.

Harmonized benchmark labels of the hippocampus on magnetic resonance: The EADC-ADNI project

A globally harmonized protocol (HarP) for manual hippocampal segmentation based on magnetic resonance has been recently developed by a task force from European Alzheimers Disease Consortium (EADC) and Alzheimers Disease Neuroimaging Initiative (ADNI). Our aim was to produce benchmark labels based on the HarP for manual segmentation.

Establishing magnetic resonance images orientation for the EADC-ADNI manual hippocampal segmentation protocol.

An effort to define and validate a Harmonized Protocol for standard hippocampal segmentation is being carried out. We wished to estimate the effect of magnetic resonance image (MRI) spatial orientation on manual hippocampal segmentations to define optimal standard orientation of MRIs for hippocampal volumetry.

Tissue-based MRI intensity standardization: application to multicentric datasets

Intensity standardization in MRI aims at correcting scanner-dependent intensity variations. Existing simple and robust techniques aim at matching the input image histogram onto a standard, while we think that standardization should aim at matching spatially corresponding tissue intensities. In this study, we present a novel automatic technique, called STI for STandardization of Intensities, which not only shares the simplicity and robustness of histogram-matching techniques, but also incorporates tissue spatial intensity information. STI uses joint intensity histograms to determine intensity correspondence in each tissue between the input and standard images. We compared STI to an existing histogram-matching technique on two multicentric datasets, Pilot E-ADNI and ADNI, by measuring the intensity error with respect to the standard image after performing nonlinear registration. The Pilot E-ADNI dataset consisted in 3 subjects each scanned in 7 different sites. The ADNI dataset consisted in 795 subjects scanned in more than 50 different sites. STI was superior to the histogram-matching technique, showing significantly better intensity matching for the brain white matter with respect to the standard image.

Manual segmentation qualification platform for the EADC-ADNI harmonized protocol for hippocampal segmentation project

The use of hippocampal volumetry as a biomarker for Alzheimer’s disease (AD) requires that tracers from different laboratories comply with the same segmentation method. Here we present a platform for training and qualifying new tracers to perform the manual segmentation of the hippocampus on magnetic resonance images (MRI) following the European Alzheimers Disease Consortium and Alzheimers Disease Neuroimaging Initiative (EADC‐ADNI) Harmonized Protocol (HarP). Our objective was to demonstrate that the training process embedded in the platform leads to increased compliance and qualification with the HarP.

Label fusion strategy selection

Label fusion is used in medical image segmentation to combine several different labels of the same entity into a single discrete label, potentially more accurate, with respect to the exact, sought segmentation, than the best input element. Using simulated data, we compared three existing label fusion techniques—STAPLE, Voting, and Shape-Based Averaging (SBA)—and observed that none could be considered superior depending on the dissimilarity between the input elements. We thus developed an empirical, hybrid technique called SVS, which selects the most appropriate technique to apply based on this dissimilarity. We evaluated the label fusion strategies on two- and three-dimensional simulated data and showed that SVS is superior to any of the three existing methods examined. On real data, we used SVS to perform fusions of 10 segmentations of the hippocampus and amygdala in 78 subjects from the ICBM dataset. SVS selected SBA in almost all cases, which was the most appropriate method overall.

Manual segmentation certification platform

We present the rationale, design, description and early user cases for a web-enabled platform for certification of manual segmentation of the hippocampus from MRIs in the context of Alzheimers disease. Preliminary thresholds serving as qualification criteria for volumetric correlation, spatial overlap and spatial distance are introduced.

Test-Retest Reliability of a New Medial Temporal Atrophy Morphological Metric

Clinicians and researchers alike are in need of quantitative and robust measurement tools to assess medial temporal lobe atrophy (MTA) due to Alzheimers disease (AD). We recently proposed a morphological metric, extracted from T1-weighted magnetic resonance images (MRI), to track and estimate MTA in cohorts of controls, AD, and mild cognitive impairment subjects, at high-risk of progression to dementia. In this paper, we investigated its reliability through analysis of within-session scan/repeat images and scan/rescans from large multicenter studies. In total, we used MRI data from 1051 subjects recruited at over 60 centers. We processed the data identically and calculated our metric for each individual, based on the concept of distance in a high-dimensional space of intensity and shape characteristics. Over 759 subjects, the scan/repeat change in the mean was 1.97% (SD: 21.2%). Over three subjects, the scan/rescan change in the mean was 0.89% (SD: 22.1%). At this level, the minimum trial size required to detect this difference is 68 individuals for both samples. Our scan/repeat and scan/rescan results demonstrate that our MTA assessment metric shows high reliability, a necessary component of validity.

Single time point high-dimensional morphometry in Alzheimer's disease: group statistics on longitudinally acquired data

Quantitative assessment of medial temporal lobe atrophy has been proposed as a biomarker for Alzheimers disease (AD) diagnostic and prognostic in mild cognitive impairment (MCI) due to AD. We present the first results of our high-dimensional morphometry technique, tracking tissue composition, and atrophy changes on T1-weighted magnetic resonance imaging at various time points. We selected 187 control subjects, 17 control subjects having progressed to MCI and/or AD, 178 subjects with stable MCI, 165 subjects with MCI having progressed to AD, and 147 AD subjects from the Alzheimers Disease Neuroimaging Initiative study. Results show statistically significant differences between almost every diagnostic and time point comparison pairs (0-12, 12-24, and 24-36 months), including controls having progressed to either MCI or AD and trajectory dynamics that demonstrate the algorithms ability at tracking specific pathology-related neurodegeneration.