Female-RHINO：一种用于自动定量子宫MRI分析和结构化报告的实时扫描仪集成框架
Female-RHINO: A Real-Time Scanner-Integrated Framework for Automated Quantitative Uterine MRI Analysis and Structured Reporting
AI总结：
提出Female-RHINO框架，集成MRI扫描仪和深度学习，实现实时自动定量子宫MRI分析及结构化报告，在500+多中心数据集上验证，处理时间<70秒。
摘要：
由于解剖变异性、观察者依赖性以及缺乏工作流集成的自动化分析工具，子宫MRI的标准化评估仍然具有挑战性。本研究提出Female-RHINO：（R）eproductive（H）ealth（I）maging A（N）alysis T（O）ol，一种在图像采集过程中用于自动定量子宫MRI分析和结构化报告的实时AI辅助框架。我们提出一个端到端系统，集成了与MRI扫描仪的在线通信和基于深度学习的分析，以从矢状T2加权盆腔MRI中推导定量子宫生物标志物。该框架结合了分割和解剖标志点检测模型，这些模型在超过500个多中心数据集上进行了训练和评估，涵盖了多种协议、供应商和患者群体。它执行体积测量，检测和量化常见的偶然发现如肌瘤和纳博特囊肿，并提取六个解剖标志点用于生物测量评估。结果被编译成结构化的临床医生导向报告，包含集成可视化，无需手动交互。在独立回顾性和前瞻性队列上的评估表明，在不同采集设置下性能稳健。子宫的平均Dice相似系数为0.82，肌瘤为0.80，纳博特囊肿的一致性较低但稳定。标志点检测的平均径向误差为3.7毫米。端到端处理在70秒内完成，使得结果可在正在进行的扫描期间获得。前瞻性部署产生了即时、标准化和可重复的分析，并得到观察者间一致性的支持。所提出的系统实现了实时扫描仪集成AI用于自动子宫MRI分析和报告，有望提高盆腔成像的标准化、效率和临床工作流程。
英文摘要：
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.
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