标题： 使用YOLO11在术中超声中实时检测脑肿瘤：从模型训练到手术室中的部署 显示英文标题

标题： Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating Room

摘要： 术中超声（ioUS）由于其多功能性、成本效益以及与手术流程的无缝集成，是脑肿瘤手术中的宝贵工具。 然而，其应用仍然有限，主要是因为图像解释方面的挑战以及有效使用所需的陡峭学习曲线。 本研究旨在通过开发一种可在手术室部署的实时脑肿瘤检测系统来提高ioUS图像的可解释性。 我们从脑肿瘤术中数据库（BraTioUS）和公开的ReMIND数据集中收集了2D ioUS图像，并用专家优化的肿瘤标签进行了标注。 使用YOLO11架构及其变体，我们训练了目标检测模型来识别脑肿瘤。 该数据集包括192名患者的1,732张图像，分为训练集、验证集和测试集。 数据增强将训练集扩展到11,570张图像。 在测试数据集中，YOLO11s在精度和计算效率之间取得了最佳平衡，mAP@50为0.95，mAP@50-95为0.65，处理速度为每秒34.16帧。 所提出的解决方案在15例连续接受手术的脑肿瘤患者队列中进行了前瞻性验证。 神经外科医生确认其与手术流程的无缝集成，实时预测准确地勾画了肿瘤区域。 这些发现突显了实时目标检测算法在增强ioUS引导的脑肿瘤手术方面的潜力，解决了解释方面的关键挑战，并为基于计算机视觉的神经肿瘤手术工具的未来发展奠定了基础。 显示英文摘要

摘要： Intraoperative ultrasound (ioUS) is a valuable tool in brain tumor surgery due to its versatility, affordability, and seamless integration into the surgical workflow. However, its adoption remains limited, primarily because of the challenges associated with image interpretation and the steep learning curve required for effective use. This study aimed to enhance the interpretability of ioUS images by developing a real-time brain tumor detection system deployable in the operating room. We collected 2D ioUS images from the Brain Tumor Intraoperative Database (BraTioUS) and the public ReMIND dataset, annotated with expert-refined tumor labels. Using the YOLO11 architecture and its variants, we trained object detection models to identify brain tumors. The dataset included 1,732 images from 192 patients, divided into training, validation, and test sets. Data augmentation expanded the training set to 11,570 images. In the test dataset, YOLO11s achieved the best balance of precision and computational efficiency, with a mAP@50 of 0.95, mAP@50-95 of 0.65, and a processing speed of 34.16 frames per second. The proposed solution was prospectively validated in a cohort of 15 consecutively operated patients diagnosed with brain tumors. Neurosurgeons confirmed its seamless integration into the surgical workflow, with real-time predictions accurately delineating tumor regions. These findings highlight the potential of real-time object detection algorithms to enhance ioUS-guided brain tumor surgery, addressing key challenges in interpretation and providing a foundation for future development of computer vision-based tools for neuro-oncological surgery.