Fully automatic deep‐learning based segmentation of Hippocampal subfields from standard resolution T1W MRI in Alzheimer’s Disease

Abstract

AbstractBackgroundHippocampal subfield volumes are of interest in Alzheimer’s disease (AD) as sub‐regions are affected at different points in the disease trajectory and may provide increased sensitivity as biomarkers compared to the whole hippocampus alone. Subfield volume estimation typically requires a high‐resolution T2‐weighted MRI to ensure accurate anatomical parcellation; however, this increases scan times and may not be present in historical datasets. Automatic methods exist for subfield segmentation from mono‐modal standard resolution T1W MRI, with deep‐learning approaches providing the most promising results to date. Here we extend such work employing deep learning approaches to provide flexible hippocampal subfield segmentation of standard resolution (1mm3) T1W MRI.MethodWe trained a collection of deep‐learning segmentation models to perform binary segmentation of the CA1‐3, CA4/DG, Subiculum, and whole Hippocampus, using a publicly available dataset of 25 subjects with manually segmented region labels on T1 MRI (1mm3). Multilabel segmentations were generated from combination of segmentation outputs.ResultTotal hippocampal and subfield multi‐label segmentations were generated for all test subjects in each cross validated loop and DICE overlap with the ground truth label computed. Preliminary results, computed using a 5‐fold cross validation, report mean DICE in whole Hippocampus = 0.904(std: 0.014), CA1‐3 = 0.842(std:0.019), CA4/DG = 0.767(std:0.04), and Subiculum = 0.791(std:0.033).Segmentation models were applied to 947 subjects with standard T1 MRI from the ADNI cohort and either a CN or MCI label. Subjects were selected with pre‐existing hippocampal subfield volumes, provided by ADNI, computed with the ASHS method using a multi‐modal T1 and hi‐res T2 MRI image. While the proposed method and ASHS employ subtly different parcellation schema we note significant correlation across similar regions between methods.Logistic regression of ICV corrected volumes and MMSE, correcting for age and sex, reveal both ASHS and the proposed method can significantly discriminate between CN and MCI groups (Table 1), with subfields areas providing greater discrimination that of the whole hippocampus.ConclusionHere we present preliminary results for a flexible deep‐learning approach to segmentation of hippocampal subfields from standard resolution T1 MRI alone; indicating comparable performance for group discrimination to an established multi‐modal standard.