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

2026-01-26

AI Summary

This study addresses the lack of fully automated, accurate, and reproducible methods for quantifying endolymphatic hydrops (EH) volume in MRI by proposing an end-to-end deep learning framework that integrates slice-level classification, inner ear localization, and multi-sequence adaptive segmentation to directly compute bilateral endolymph-to-vestibule volume ratios (ELR) from 3D MRI scans—compatible with both 3D-SPACE-MRC and 3D-REAL-IR protocols. Notably, the method achieves generalization to full 3D volumes using only 3–6 annotated slices per case, substantially reducing annotation burden. External validation demonstrates a volume similarity index (VSI) of 74.3%, markedly outperforming the clinical syngo.via software (42.5%) and yielding EH quantification results more consistent with physiological reality.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: 3D Computer Vision
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

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.

Problem

• endolymphatic hydrops
• volumetric quantification
• MRI
• automated pipeline
• inner ear

Innovation

• endolymphatic hydrops
• deep learning
• fully automated pipeline
• volumetric quantification
• MRI segmentation