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

• URL: http://arxiv.org/abs/2501.15994v1
• Date: Mon, 27 Jan 2025 12:29:19 GMT
• Title: Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating Room
• Authors: Santiago Cepeda, Olga Esteban-Sinovas, Roberto Romero, Vikas Singh, Prakash Shetty, Aliasgar Moiyadi, Ilyess Zemmoura, Giuseppe Roberto Giammalva, Massimiliano Del Bene, Arianna Barbotti, Francesco DiMeco, Timothy R. West, Brian V. Nahed, Ignacio Arrese, Roberto Hornero, Rosario Sarabia,
• Abstract summary: Intraoperative ultrasound (ioUS) is a valuable tool in brain tumor surgery due to its versatility, affordability, and seamless integration into the surgical workflow. 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.
• Score: 13.503074690080453
• License:
• Abstract: 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.

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