论文标题
从机器人手术视频中进行半监督仪器分割的学习运动流动
Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video
论文作者
论文摘要
以较低的赫兹标签进行手术视频，以大大释放外科医生的负担。在本文中，我们从具有稀疏注释的机器人手术视频中研究了半监督仪器分割。与大多数以前使用未标记框架单独使用的方法不同，我们提出了一种基于双运动的方法，可以通过利用时间动力学来明智地学习运动流以进行分割增强。我们首先设计一个流预测变量，以得出鉴于当前标记的帧的联合传播框架标签对的运动。考虑到快速仪器的运动，我们进一步引入了一个流动补偿器，以通过新颖的周期学习策略在连续框架内估算中间运动。通过利用生成的数据对，我们的框架可以恢复甚至增强训练序列的时间一致性，从而使分段受益。我们使用2017年Miccai Endovis机器人仪器分割挑战数据集的二进制，部分和类型任务来验证我们的框架。结果表明，我们的方法的表现优于最先进的半监督方法，甚至超过了对两项任务的全面监督培训。
Performing low hertz labeling for surgical videos at intervals can greatly releases the burden of surgeons. In this paper, we study the semi-supervised instrument segmentation from robotic surgical videos with sparse annotations. Unlike most previous methods using unlabeled frames individually, we propose a dual motion based method to wisely learn motion flows for segmentation enhancement by leveraging temporal dynamics. We firstly design a flow predictor to derive the motion for jointly propagating the frame-label pairs given the current labeled frame. Considering the fast instrument motion, we further introduce a flow compensator to estimate intermediate motion within continuous frames, with a novel cycle learning strategy. By exploiting generated data pairs, our framework can recover and even enhance temporal consistency of training sequences to benefit segmentation. We validate our framework with binary, part, and type tasks on 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset. Results show that our method outperforms the state-of-the-art semi-supervised methods by a large margin, and even exceeds fully supervised training on two tasks.
