Deep-learning assessment of hippocampal magnetic susceptibility in Alzheimer's disease.

Abstract

Background: Quantitative susceptibility mapping (QSM) is pivotal for analyzing neurodegenerative diseases. However, accurate hippocampal segmentation remains a challenge.

Objective: This study introduces a method for extracting hippocampal magnetic susceptibility values using a convolutional neural network (CNN) model referred to as 3D residual UNET.

Methods: The model was pre-trained on whole QSM images and hippocampal segmentations from 3D T1-weighted images of 297 patients with Alzheimer's disease and mild cognitive impairment. Fine-tuning was conducted through manually annotated hippocampal segmentations from the QSM images of 60 patients. The performance was assessed using the Dice similarity coefficient (DSC) and Pearson correlation coefficient.

Results: The developed model was applied to another 98 patients, 49 with AD and 49 with mild cognitive impairment (MCI), and the correlation between the hippocampal magnetic susceptibility and volume was evaluated. The mean DSC for the hippocampal segmentation model was 0.716 ± 0.045. The correlation coefficient between the magnetic susceptibility values derived from manual segmentation and the CNN model was 0.983. The Pearson correlation coefficient between magnetic susceptibility and hippocampal volume from the CNN model was -0.252 (p = 0.012) on the left side and -0.311 (p = 0.002) on the right.

Conclusions: The 3D residual UNET model enhances hippocampal analysis precision using QSM, which is capable of accurately extracting magnetic susceptibility.