A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI

Michael Rebsamen; Piotr Radojewski; Richard McKinley; Mauricio Reyes; Roland Wiest; Christian Rummel

doi:10.3389/fneur.2022.812432

A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI

Front Neurol. 2022 Feb 18:13:812432. doi: 10.3389/fneur.2022.812432. eCollection 2022.

Authors

Michael Rebsamen^{1

2}, Piotr Radojewski^{1

3}, Richard McKinley¹, Mauricio Reyes⁴, Roland Wiest^{1

3}, Christian Rummel¹

Affiliations

¹ Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
² Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland.
³ Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland.
⁴ ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland.

Abstract

Purpose: Hippocampal volumetry is an important biomarker to quantify atrophy in patients with mesial temporal lobe epilepsy. We investigate the sensitivity of automated segmentation methods to support radiological assessments of hippocampal sclerosis (HS). Results from FreeSurfer and FSL-FIRST are contrasted to a deep learning (DL)-based segmentation method.

Materials and methods: We used T1-weighted MRI scans from 105 patients with epilepsy and 354 healthy controls. FreeSurfer, FSL, and a DL-based method were applied for brain anatomy segmentation. We calculated effect sizes (Cohen's d) between left/right HS and healthy controls based on the asymmetry of hippocampal volumes. Additionally, we derived 14 shape features from the segmentations and determined the most discriminating feature to identify patients with hippocampal sclerosis by a support vector machine (SVM).

Results: Deep learning-based segmentation of the hippocampus was the most sensitive to detecting HS. The effect sizes of the volume asymmetries were larger with the DL-based segmentations (HS left d= -4.2, right = 4.2) than with FreeSurfer (left= -3.1, right = 3.7) and FSL (left= -2.3, right = 2.5). For the classification based on the shape features, the surface-to-volume ratio was identified as the most important feature. Its absolute asymmetry yielded a higher area under the curve (AUC) for the deep learning-based segmentation (AUC = 0.87) than for FreeSurfer (0.85) and FSL (0.78) to dichotomize HS from other epilepsy cases. The robustness estimated from repeated scans was statistically significantly higher with DL than all other methods.

Conclusion: Our findings suggest that deep learning-based segmentation methods yield a higher sensitivity to quantify hippocampal sclerosis than atlas-based methods and derived shape features are more robust. We propose an increased asymmetry in the surface-to-volume ratio of the hippocampus as an easy-to-interpret quantitative imaging biomarker for HS.

Keywords: MRI; brain morphometry; deep learning; epilepsy; hippocampal sclerosis; hippocampus; segmentation.