Instituto de las Tecnologías Avanzadas de la Producción (ITAP), Universidad de Valladolid, Paseo del Cauce 59, 47011 Valladolid, Spain.
Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Calle de José Gutiérrez Abascal, 2, 28006 Madrid, Spain.
Sensors (Basel). 2022 Jul 11;22(14):5180. doi: 10.3390/s22145180.
Medical instruments detection in laparoscopic video has been carried out to increase the autonomy of surgical robots, evaluate skills or index recordings. However, it has not been extended to surgical gauzes. Gauzes can provide valuable information to numerous tasks in the operating room, but the lack of an annotated dataset has hampered its research. In this article, we present a segmentation dataset with 4003 hand-labelled frames from laparoscopic video. To prove the dataset potential, we analyzed several baselines: detection using YOLOv3, coarse segmentation, and segmentation with a U-Net. Our results show that YOLOv3 can be executed in real time but provides a modest recall. Coarse segmentation presents satisfactory results but lacks inference speed. Finally, the U-Net baseline achieves a good speed-quality compromise running above 30 FPS while obtaining an IoU of 0.85. The accuracy reached by U-Net and its execution speed demonstrate that precise and real-time gauze segmentation can be achieved, training convolutional neural networks on the proposed dataset.
已经进行了腹腔镜视频中的医疗器械检测，以提高手术机器人的自主性、评估技能或索引记录。然而，它尚未扩展到手术纱布。纱布可以为手术室中的众多任务提供有价值的信息，但缺乏标注数据集阻碍了其研究。在本文中，我们提出了一个包含 4003 个腹腔镜视频手标记帧的分割数据集。为了证明数据集的潜力，我们分析了几个基线：使用 YOLOv3 进行检测、粗分割和使用 U-Net 进行分割。我们的结果表明，YOLOv3 可以实时执行，但召回率不高。粗分割的结果令人满意，但缺乏推理速度。最后，U-Net 基线在 30 FPS 以上运行时能够实现良好的速度-质量折衷，同时获得 0.85 的 IoU。U-Net 的准确性和执行速度表明，可以在提出的数据集上训练卷积神经网络，从而实现精确和实时的纱布分割。
