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Abstract：
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.
摘要：
部分自动化机器人系统，如相机支架，代表了提高手术效率和精度的关键一步。因此,本文介绍了一种使用卷积神经网络在腹腔镜手术中实时工具定位的方法。提出的模型，基于两个串联的沙漏模块，可以同时定位两个手术工具。这项研究利用了三个数据集：ITAP数据集，除了两个公开可用的数据集，即AtlasDione和EndoVis挑战赛。提出了基于沙漏的模型的三种变体，使用最佳模型实现高精度(92.86%)和帧速率(27.64FPS)，适合集成到机器人系统。对独立测试集的评估得出的准确性略低，表明泛化性有限。使用Grad-CAM技术进一步分析了该模型，以深入了解其功能。总的来说,这项工作为腹腔镜手术的自动化方面提出了一个有希望的解决方案，通过减少手动内窥镜操作的需要，有可能提高手术效率。
