Development of an isotropic segmentation model for medial temporal lobe subregions on anisotropic MRI atlas using implicit neural representation
This work addresses the need for more accurate Alzheimer's disease imaging biomarkers by improving segmentation without extra annotation, potentially aiding in better disease tracking and quantification of brain atrophy relationships.
The study tackled the problem of accurately segmenting medial temporal lobe subregions on anisotropic MRI by using implicit neural representation to upsample an atlas to isotropic space, resulting in a model that improved biomarker significance in distinguishing mild cognitive impairment from cognitively unimpaired participants and showed greater stability in longitudinal analysis.
Imaging biomarkers in magnetic resonance imaging (MRI) are important tools for diagnosing and tracking Alzheimer's disease (AD). As medial temporal lobe (MTL) is the earliest region to show AD-related hallmarks, brain atrophy caused by AD can first be observed in the MTL. Accurate segmentation of MTL subregions and extraction of imaging biomarkers from them are important. However, due to imaging limitations, the resolution of T2-weighted (T2w) MRI is anisotropic, which makes it difficult to accurately extract the thickness of cortical subregions in the MTL. In this study, we used an implicit neural representation method to combine the resolution advantages of T1-weighted and T2w MRI to accurately upsample an MTL subregion atlas set from anisotropic space to isotropic space, establishing a multi-modality, high-resolution atlas set. Based on this atlas, we developed an isotropic MTL subregion segmentation model. In an independent test set, the cortical subregion thickness extracted using this isotropic model showed higher significance than an anisotropic method in distinguishing between participants with mild cognitive impairment and cognitively unimpaired (CU) participants. In longitudinal analysis, the biomarkers extracted using isotropic method showed greater stability in CU participants. This study improved the accuracy of AD imaging biomarkers without increasing the amount of atlas annotation work, which may help to more accurately quantify the relationship between AD and brain atrophy and provide more accurate measures for disease tracking.