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摘要翻译：
在目前的机器人辅助微创手术系统中，由于手术环境的限制，提供力反馈作为相关信息构成了一个技术挑战。在这种情况下，无传感器力估计技术代表了一种潜在的解决方案，能够感知手术器械和软组织之间的相互作用力。具体地说，如果视觉反馈可以用来观察软组织的变形，这种反馈可以用来估计施加在这些组织上的力。为此，本文提出了一种基于卷积神经网络和长短期记忆网络的力估计模型。该模型既处理视频序列中的时空信息，又处理刀具数据的时间结构（刀尖轨迹和刀尖抓取状态）。通过一系列分析，揭示了该方案的优点和实际应用中仍然存在的挑战。这项研究工作集中在两个手术任务场景，简称推组织和拉组织。针对这两种情况，研究了不同的输入数据模式及其对力估计质量的影响。这些输入数据模式是工具数据、视频序列以及两者的组合。结果表明，采用神经网络模型处理刀具数据和视频序列时，力估计质量较好。此外，这项研究揭示了损失函数的必要性，旨在促进在力信号中发现的平滑和尖锐细节的建模。最后，结果表明，由于拉力任务的建模比最简单的推动行动更具挑战性。
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英文标题：
《A Recurrent Convolutional Neural Network Approach for Sensorless Force
Estimation in Robotic Surgery》
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作者：
Arturo Marban, Vignesh Srinivasan, Wojciech Samek, Josep Fern\'andez,
Alicia Casals
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最新提交年份：
2018
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Neural and Evolutionary Computing 神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
Providing force feedback as relevant information in current Robot-Assisted Minimally Invasive Surgery systems constitutes a technological challenge due to the constraints imposed by the surgical environment. In this context, Sensorless Force Estimation techniques represent a potential solution, enabling to sense the interaction forces between the surgical instruments and soft-tissues. Specifically, if visual feedback is available for observing soft-tissues' deformation, this feedback can be used to estimate the forces applied to these tissues. To this end, a force estimation model, based on Convolutional Neural Networks and Long-Short Term Memory networks, is proposed in this work. This model is designed to process both, the spatiotemporal information present in video sequences and the temporal structure of tool data (the surgical tool-tip trajectory and its grasping status). A series of analyses are carried out to reveal the advantages of the proposal and the challenges that remain for real applications. This research work focuses on two surgical task scenarios, referred to as pushing and pulling tissue. For these two scenarios, different input data modalities and their effect on the force estimation quality are investigated. These input data modalities are tool data, video sequences and a combination of both. The results suggest that the force estimation quality is better when both, the tool data and video sequences, are processed by the neural network model. Moreover, this study reveals the need for a loss function, designed to promote the modeling of smooth and sharp details found in force signals. Finally, the results show that the modeling of forces due to pulling tasks is more challenging than for the simplest pushing actions.
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PDF链接：
https://arxiv.org/pdf/1805.08545
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