Minimally invasive robotic interventions have highlighted the need to develop efficient techniques to measure forces applied to the soft tissues. Since the last decade, many scholars have focused on micro-scale and macro-scale robotic manipulations. Early articles used the model of soft tissue mathematically and tracked the displacement of the contour of the object in the vision system to provide the corresponding force to the user. Lack of knowledge of different materials and the computational complexity led to a transition from model-based to learning-based approaches to interpret the relation between object deformations, extracted from the vision system, and the real forces applied to the object. The dramatic growth of machine learning techniques and its integration with computer vision has brought novel learning-based visual data processing methods to the area. The application of the image-based force estimation methods in a controlled medical intervention has also received significant attention in the last five years. A decent number of surveys have been published on micromanipulation in recent years, especially for cell microinjection. However, the state of the art in meso- and macro-scale medical robotic interventions has not been reviewed. The aim and contribution of this paper are to fill the stated gap by reviewing the recent advances in image-based force estimation in robotic interventions. The survey shows that learning-based force estimation methods are growing significantly by using deep learning-based methods. The survey will encourage researchers and surgeons to apply learning-based algorithms to real-time medical and health-related operations.

中文翻译：

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基于图像的医疗应用力估计：综述

微创机器人干预已强调需要开发有效的技术来测量施加在软组织上的力。自上个十年以来，许多学者一直专注于微观和宏观机器人操纵。早期的文章在数学上使用了软组织模型，并在视觉系统中跟踪了对象轮廓的位移，以向用户提供相应的力。缺乏对不同材料的了解以及计算复杂性导致从基于模型的方法过渡到基于学习的方法，以解释从视觉系统提取的对象变形与施加到对象的实际力之间的关系。机器学习技术的迅猛发展及其与计算机视觉的集成为该领域带来了新颖的基于学习的视觉数据处理方法。在过去的五年中，基于图像的力估计方法在受控医疗干预中的应用也受到了极大的关注。近年来，有关显微操作的大量研究已经发表，特别是对于细胞显微注射。但是，中小型和大型医疗机器人干预的最新技术尚未得到审查。本文的目的和贡献是通过回顾机器人干预中基于图像的力估计的最新进展来填补上述空白。调查显示，通过使用基于深度学习的方法，基于学习的力估计方法正在显着增长。