Instituto de las Tecnologías Avanzadas de la Producción (ITAP), Escuela de Ingenierías Industriales, Universidad de Valladolid, Paseo Prado de la Magdalena 3-5, 47011 Valladolid, Spain.
Sensors (Basel). 2024 Jun 27;24(13):4191. doi: 10.3390/s24134191.
Partially automated robotic systems, such as camera holders, represent a pivotal step towards enhancing efficiency and precision in surgical procedures. Therefore, this paper introduces an approach for real-time tool localization in laparoscopy surgery using convolutional neural networks. The proposed model, based on two Hourglass modules in series, can localize up to two surgical tools simultaneously. This study utilized three datasets: the ITAP dataset, alongside two publicly available datasets, namely Atlas Dione and EndoVis Challenge. Three variations of the Hourglass-based models were proposed, with the best model achieving high accuracy (92.86%) and frame rates (27.64 FPS), suitable for integration into robotic systems. An evaluation on an independent test set yielded slightly lower accuracy, indicating limited generalizability. The model was further analyzed using the Grad-CAM technique to gain insights into its functionality. Overall, this work presents a promising solution for automating aspects of laparoscopic surgery, potentially enhancing surgical efficiency by reducing the need for manual endoscope manipulation.
部分自动化机器人系统，如摄像器固定架，代表了在手术过程中提高效率和精度的关键步骤。因此，本文提出了一种使用卷积神经网络进行腹腔镜手术中实时工具定位的方法。所提出的模型基于串联的两个 Hourglass 模块，可同时定位多达两个手术工具。本研究使用了三个数据集：ITAP 数据集，以及两个公开可用的数据集，即 Atlas Dione 和 EndoVis Challenge。提出了三种基于 Hourglass 的模型变体，最佳模型实现了高精度（92.86%）和帧率（27.64 FPS），适合集成到机器人系统中。在独立测试集上的评估结果准确性略低，表明通用性有限。该模型进一步使用 Grad-CAM 技术进行了分析，以深入了解其功能。总的来说，这项工作为自动化腹腔镜手术的某些方面提供了一个有前途的解决方案，通过减少对手动内窥镜操作的需求，有可能提高手术效率。
