Alzheimer’s disease diagnosis based on the Hippocampal Unified Multi-Atlas Network (HUMAN) algorithm

Background Hippocampal atrophy is a supportive feature for the diagnosis of probable Alzheimer’s disease (AD). However, even for an expert neuroradiologist, tracing the hippocampus and measuring its volume is a time consuming and extremely challenging task. Accordingly, the development of reliable f...

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Vydané v:Biomedical engineering online Ročník 17; číslo 1; s. 6 - 16
Hlavní autori: Amoroso, Nicola, Rocca, Marianna La, Bellotti, Roberto, Fanizzi, Annarita, Monaco, Alfonso, Tangaro, Sabina
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 22.01.2018
BioMed Central Ltd
BMC
Predmet:
Advances in neuroimaging: Insights into neurological and psychiatric disease
Algorithms
Alzheimer's disease
Biomaterials
Biomedical Engineering and Bioengineering
Biomedical Engineering/Biotechnology
Biotechnology
Diagnosis
Engineering
Hippocampal Segmentation
MCI
Multi-atlas
Neural Networks
Patient outcomes
Sclerosis
Multi-atlas
Neural Networks
MCI
Hippocampal Segmentation
Alzheimer’s disease
ISSN:1475-925X, 1475-925X
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Shrnutí:Background Hippocampal atrophy is a supportive feature for the diagnosis of probable Alzheimer’s disease (AD). However, even for an expert neuroradiologist, tracing the hippocampus and measuring its volume is a time consuming and extremely challenging task. Accordingly, the development of reliable fully-automated segmentation algorithms is of paramount importance. Materials and methods The present study evaluates (i) the precision and the robustness of the novel Hippocampal Unified Multi-Atlas Network (HUMAN) segmentation algorithm and (ii) its clinical reliability for AD diagnosis. For these purposes, we used a mixed cohort of 456 subjects and their T1 weighted magnetic resonance imaging (MRI) brain scans. The cohort included 145 controls (CTRL), 217 mild cognitive impairment (MCI) subjects and 94 AD patients from Alzheimer’s Disease Neuroimaging Initiative (ADNI). For each subject the baseline, repeat, 12 and 24 month follow-up scans were available. Results HUMAN provides hippocampal volumes with a 3% precision; volume measurements effectively reveal AD, with an area under the curve (AUC) AUC 1  = 0.08 ± 0.02. Segmented volumes can also reveal the subtler effects present in MCI subjects, AUC 2  = 0.76 ± 0.05. The algorithm is stable and reproducible over time, even for 24 month follow-up scans. Conclusions The experimental results demonstrate HUMAN is a precise segmentation algorithm, besides hippocampal volumes, provided by HUMAN, can effectively support the diagnosis of Alzheimer’s disease and become a useful tool for other neuroimaging applications.
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ISSN:1475-925X
1475-925X
DOI:10.1186/s12938-018-0439-y