Female-RHINO: A Real-Time Scanner-Integrated Framework for Automated Quantitative Uterine MRI Analysis and Structured Reporting

2026-06-23

AI Summary

Standardized assessment of uterine MRI is hindered by substantial anatomical variability, high inter-observer dependency, and the absence of automated tools. This work proposes the first end-to-end real-time artificial intelligence framework that integrates deep learning–driven segmentation of the uterus and lesions, detection of anatomical landmarks, and in-scanner communication to enable automatic quantitative analysis and structured report generation during image acquisition. Designed for multi-center, multi-vendor, and multi-protocol compatibility, the system has been validated on independent retrospective and prospective cohorts, achieving Dice coefficients of 0.82 and 0.80 for uterine and fibroid segmentation, respectively, with a mean radial error of 3.7 mm in landmark localization. The entire pipeline completes in under 70 seconds, enabling, for the first time, immediate delivery of standardized reports during MRI scanning.

Abstract

Standardized assessment of uterine MRI remains challenging due to anatomical variability, observer dependence, and the lack of workflow-integrated automated analysis tools. This work presents Female-RHINO: (R)eproductive (H)ealth (I)maging A(N)alysis T(O)ol, a real-time AI-assisted framework for automated quantitative uterine MRI analysis and structured reporting during image acquisition. We present an end-to-end system that integrates inline communication with the MRI scanner and deep learning-based analysis to derive quantitative uterine biomarkers from sagittal T2-weighted pelvic MRI. The framework combines segmentation and anatomical landmark detection models trained and evaluated on more than 500 multi-center datasets spanning diverse protocols, vendors, and patient populations. It performs volumetry, detects and quantifies common incidental findings such as fibroids and Nabothian cysts, and extracts six anatomical landmarks for biometric assessment. Results are compiled into a structured clinician-oriented report with integrated visualizations, without manual interaction. Evaluation on independent retrospective and prospective cohorts demonstrated robust performance across varying acquisition settings. Mean Dice similarity coefficients were 0.82 for the uterus and 0.80 for fibroids, with lower but consistent agreement for Nabothian cysts. Landmark detection achieved a mean radial error of 3.7 mm. End-to-end processing was completed in under 70 seconds, enabling availability of results during the ongoing scan. Prospective deployment yielded immediate, standardized, and reproducible analyses supported by inter-observer agreement. The proposed system enables real-time scanner-integrated AI for automated uterine MRI analysis and reporting, with potential to improve standardization, efficiency, and clinical workflow in pelvic imaging.

Problem

• uterine MRI
• standardized assessment
• anatomical variability
• observer dependence
• automated analysis

Innovation

• real-time AI
• scanner-integrated framework
• automated uterine MRI analysis
• deep learning segmentation
• structured reporting