全自动量化耳内淋巴积水，仅需少量标注即可精准分析标准MRI。
OREHAS: A fully automated deep-learning pipeline for volumetric endolymphatic hydrops quantification in MRI
- 三步流程整合：切片分类、耳部定位、序列特异性分割，端到端自动计算体积比。
- 在真实数据上达到0.90（SPACE-MRC）和0.75（REAL-IR）的分割准确率。
- 结果更接近专家标注，适合临床研究与大规模筛查使用。
我们提出OREHAS（优化识别与评估听觉系统内淋巴积水），首个可完全自动化地从常规3D-SPACE-MRC和3D-REAL-IR MRI中定量内淋巴积水的深度学习流程。该系统将切片分类、内耳定位与序列特定分割三部分集成于单一工作流，直接从全幅MRI体积计算单耳内淋巴腔与前庭腔体积比（ELR），无需人工干预。每例仅需3至6个标注切片训练，即能有效泛化至完整3D体积，在SPACE-MRC和REAL-IR上分别取得0.90和0.75的Dice评分。在包含完整人工标注的外部验证队列中，OREHAS与专家基准高度一致（VSI=74.3%），显著优于临床软件syngo.via（VSI=42.5%），后者易高估内淋巴体积。19名测试患者中，前庭测量与syngo.via一致，但内淋巴体积系统性更小且更符合生理实际。结果表明，仅需有限监督即可实现可靠、可重复的内淋巴积水量化。通过高效深度学习分割与临床兼容的体积工作流结合，OREHAS降低操作依赖，保证方法一致性，并兼容现有成像协议。该方法为大规模研究及基于精确体积测量的临床诊断阈值重校准提供坚实基础。
原文摘要 · Abstract (English)
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.
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