ArticleBiomedical engineering online2018
Alzheimer's disease diagnosis based on the Hippocampal Unified Multi-Atlas Network (HUMAN) algorithm.
Article in Biomedical engineering online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Improved Multiscale Structural Mapping with Supervertex Vision Transformer for the Detection of Alzheimer's Disease Neurodegeneration.Human brain mapping · 2026Article
- Classification method based on surf and sift features for alzheimer diagnosis using diffusion tensor magnetic resonance imaging.Scientific reports · 2025Article
- Hierarchical based classification method based on fusion of Gaussian map descriptors for Alzheimer diagnosis using TScientific reports · 2023Article
- Explainable machine learning with pairwise interactions for the classification of Parkinson's disease and SWEDD from clinical and imaging features.Brain imaging and behavior · 2022Article
- Classification of Alzheimer's Disease Based on Abnormal Hippocampal Functional Connectivity and Machine Learning.Frontiers in aging neuroscience · 2022Article
- Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis.Frontiers in public health · 2022Article
- Applications of machine learning to diagnosis and treatment of neurodegenerative diseases.Nature reviews. Neurology · 2020Review
- Multiplex Networks to Characterize Seizure Development in Traumatic Brain Injury Patients.Frontiers in neuroscience · 2020Article
- Machine learning studies on major brain diseases: 5-year trends of 2014-2018.Japanese journal of radiology · 2019Review
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Authors and funding
7 authors.
Funding
Abstract
backgroundHippocampal 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
methodsThe 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.
resultsHUMAN provides hippocampal volumes with a 3% precision; volume measurements effectively reveal AD, with an area under the curve (AUC) AUC
conclusionsThe 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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