OREHAS: A fully automated deep-learning pipeline for volumetric endolymphatic hydrops quantification in MRI

TL;DR

OREHAS is an automated deep-learning pipeline that accurately quantifies endolymphatic hydrops in MRI, reducing manual effort and outperforming existing clinical software, thus enabling large-scale and consistent inner ear assessments.

Contribution

This work introduces the first fully automatic pipeline for volumetric EH quantification from routine MRI, using limited supervision and integrating multiple components into a single workflow.

Findings

Achieved Dice scores of 0.90 for SPACE-MRC and 0.75 for REAL-IR.

Outperformed clinical software in external validation, with VSI of 74.3% versus 42.5%.

Produced physiologically realistic endolymphatic volume measurements.

Abstract

We present OREHAS (Optimized Recognition & Evaluation of volumetric Hydrops in the Auditory System), the first fully automatic pipeline for volumetric quantification of endolymphatic hydrops (EH) from routine 3D-SPACE-MRC and 3D-REAL-IR MRI. The system integrates three components -- slice classification, inner ear localization, and sequence-specific segmentation -- into a single workflow that computes per-ear endolymphatic-to-vestibular volume ratios (ELR) directly from whole MRI volumes, eliminating the need for manual intervention. Trained with only 3 to 6 annotated slices per patient, OREHAS generalized effectively to full 3D volumes, achieving Dice scores of 0.90 for SPACE-MRC and 0.75 for REAL-IR. In an external validation cohort with complete manual annotations, OREHAS closely matched expert ground truth (VSI = 74.3%) and substantially outperformed the clinical syngo.via software (VSI = 42.5%), which tended to overestimate endolymphatic volumes. Across 19 test patients, vestibular measurements from OREHAS were consistent with syngo.via, while endolymphatic volumes were systematically smaller and more physiologically realistic. These results show that reliable and reproducible EH quantification can be achieved from standard MRI using limited supervision. By combining efficient deep-learning-based segmentation with a clinically aligned volumetric workflow, OREHAS reduces operator dependence, ensures methodological consistency. Besides, the results are compatible with established imaging protocols. The approach provides a robust foundation for large-scale studies and for recalibrating clinical diagnostic thresholds based on accurate volumetric measurements of the inner ear.