论文标题
在机器人手术中寻找有效的仪器进行仪器分割
Searching for Efficient Architecture for Instrument Segmentation in Robotic Surgery
论文作者
论文摘要
手术仪器的分割是机器人辅助手术中的一个重要问题：这是朝着完整仪器估算的关键步骤，直接用于掩盖手术过程中增强现实叠加层的掩饰。大多数应用都依赖于高分辨率手术图像的准确实时分割。尽管以前的研究主要集中在提供高精度分割面罩的方法上，但由于其计算成本，大多数都不能用于实时应用。在这项工作中，我们设计了一种轻巧且高效的深残留体系结构，该体系结构被调整为对高分辨率图像的实时推断。为了说明发现的轻量级残留网络的精度降低，并避免增加任何其他计算负担，我们对网络残留单位的扩张率进行了可区分的搜索。我们在Endovis 2017机器人仪器数据集上测试了我们发现的架构，并验证我们的模型在速度和准确性折衷方面是最先进的，在高分辨率图像上的速度高达125 fps。
Segmentation of surgical instruments is an important problem in robot-assisted surgery: it is a crucial step towards full instrument pose estimation and is directly used for masking of augmented reality overlays during surgical procedures. Most applications rely on accurate real-time segmentation of high-resolution surgical images. While previous research focused primarily on methods that deliver high accuracy segmentation masks, majority of them can not be used for real-time applications due to their computational cost. In this work, we design a light-weight and highly-efficient deep residual architecture which is tuned to perform real-time inference of high-resolution images. To account for reduced accuracy of the discovered light-weight deep residual network and avoid adding any additional computational burden, we perform a differentiable search over dilation rates for residual units of our network. We test our discovered architecture on the EndoVis 2017 Robotic Instruments dataset and verify that our model is the state-of-the-art in terms of speed and accuracy tradeoff with a speed of up to 125 FPS on high resolution images.
