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Morphometry of medial temporal lobe (MTL) subregions in brain MRI issensitive biomarker to Alzheimers Disease and other related conditions. WhileT2-weighted (T2w) MRI with high in-plane resolution is widely used to segmenthippocampal subfields due to its higher contrast in hippocampus, its lowerout-of-plane resolution reduces the accuracy of subregion thicknessmeasurements. To address this issue, we developed a nearly isotropicsegmentation pipeline that incorporates image and label upsampling andhigh-resolution segmentation in T2w MRI. First, a high-resolution atlas wascreated based on an existing anisotropic atlas derived from 29 individuals.Both T1-weighted and T2w images in the atlas were upsampled from their originalresolution to a nearly isotropic resolution 0.4x0.4x0.52mm3 using a non-localmeans approach. Manual segmentations within the atlas were also upsampled tomatch this resolution using a UNet-based neural network, which was trained on acohort consisting of both high-resolution ex vivo and low-resolutionanisotropic in vivo MRI with manual segmentations. Second, a multi-modalitydeep learning-based segmentation model was trained within this nearly isotropicatlas. Finally, experiments showed the nearly isotropic subregion segmentationimproved the accuracy of cortical thickness as an imaging biomarker forneurodegeneration in T2w MRI.

Nearly isotropic segmentation for medial temporal lobe subregions in multi-modality MRI