Background: Alzheimer’s disease (AD) is an emerging public health crisis and biomarkers are playing a large role in AD research. Magnetic Resonance Imaging (MRI) holds advantages over existing biomarkers for AD. This project aims to measure subfield thickness throughout the hippocampal long axis using HippUnfold, a novel open-source automated hippocampal segmentation software. Methods: High resolution (0.39×0.39×2mm) Hippocampal MR Images [control, n= 16, mild cognitive impairment (MCI, n =16), and AD, (n = 16)] acquired by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were analyzed with an automated segmentation software (HippUnfold) to compute thickness measurements. ADNI data such as Positron Emission Tomography (PET) biomarkers, Cerebrospinal Fluid biomarkers, and cognitive scores such as Mini-Mental State Exam (MMSE), Montreal Cognitive Assessment (MoCA), Alzheimer’s Disease Assessment Scale (ADAS13), and Rey Auditory Verbal Learning Test (RAVLT), were correlated to thickness along the hippocampal long axis using linear regression models. Results: We found significant cluster correlations (p < 0.05) throughout the long axis between hippocampal subfield thickness to MoCA scores, ADAS13 scores, PET phosphorylated tau levels, and PET beta-amyloid levels. Conclusions: Subfield atrophy throughout the hippocampal long axis is associated with disease severity (as measured with existing biomarkers and cognitive testing) in patients with MCI and AD.