An Hippocampal Segmentation Tool Within an Open Cloud Infrastructure

This study presents a fully automated algorithm for the segmentation of the hippocampus in structural Magnetic Resonance Imaging (MRI) and its deployment as a service on an open cloud infrastructure. Optimal atlases strategies for multi-atlas learning are combined with a voxel-wise classification approach. The method efficiency is optimized as training atlases are previously registered to a data driven template, accordingly for each test MRI scan only a registration is needed. The selected optimal atlases are used to train dedicated random forest classifiers whose labels are fused by majority voting. The method performances were tested on a set of 100 MRI scans provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Leave-one-out results (Dice = \(0.910\,\pm \,0.004\)) show the presented method compares well with other state-of-the-art techniques and a benchmark segmentation tool as FreeSurfer. The proposed strategy significantly improves a standard multi-atlas approach (\(p < .001\)).