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An Extremely Fast and Precise Convolutional Neural Network for Recognition and Localization of Cataract Surgical Tools

机译:一种极快而精确的卷积神经网络,用于识别和定位白内障手术工具

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Recognition and localization of surgical tools is a crucial requirement to provide safe tool-tissue interaction in various computer-assisted interventions (CAI). Unfortunately, most state-of-the-art approaches are committed to improving detection precision regardless of the real-time performance, which leads to poor prediction for these methods in intraoperative detection task. In this paper, we propose an extremely fast and precise network (EF-PNet) for tool detection that performs well both in intraoperative tracking and postoperative skill evaluation. The proposed approach takes a single sweep of the single network to achieve rapid tool detection during intraoperative tasks, and also integrates densely connected constraint to guarantee a comparable precision for skill assessment. We demonstrate the superiority of our method on a newly built dataset: cataract surgical tool location (CaSToL). Experimental results with a mean inference time of 3.7 ms per test frame detection (i.e. 270 fps) and a mean average precision (mAP) of 93%, demonstrate the effectiveness of the proposed architecture, and also indicate that our study is far superior to recent region-based methods for tool detection in terms of detection speed, surely with a comparable precision.
机译:外科手术工具的识别和定位是提供各种计算机辅助干预(CAI)中的安全工具组织相互作用的重要要求。不幸的是,无论实时性能如何,大多数最先进的方法都致力于提高检测精度,这导致术中检测任务中这些方法的预测不良。在本文中,我们提出了一种非常快速和精确的网络(EF-PENT),用于术中跟踪和术后技能评估。所提出的方法采用单一网络的单一扫描来实现术中任务期间的快速工具检测,并且还集成了密集连接的约束,以确保技能评估的可比精度。我们展示了我们在新建数据集中的方法的优越性:白内障手术工具位置(Castol)。每个测试帧检测(即270 fps)的平均推理时间为3.7ms的实验结果,平均平均精度(地图)为93%,展示了拟议的架构的有效性,并表明我们的研究远远优于最近基于区域的刀具检测方法,依全​​有相当的精度。

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