A recurrent convolutional neural network approach for sensorless force estimation in robotic surgery. (April 2019)

• Record Type: Journal Article
• Authors: Marban, Arturo; Srinivasan, Vignesh; Samek, Wojciech; Fernández, Josep; Casals, Alicia
• Is Part Of: Biomedical signal processing and control. Volume 50(2019)
• Page Start: 134
• Page End: 150
• Publication Date: 2019-04
• Subjects: Robotic surgery -- Force estimation -- Convolutional neural networks -- LSTM networks; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.01.011
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Sensorless Force Estimation techniques represent a potential solution to restore force feedback as a feature in current Robot-Assisted Minimally Invasive Surgery systems. A model based on Convolutional Neural Networks and Long-Short Term Memory networks represents a potential approach to sense the interaction forces between the surgical instruments and soft-tissues. Different data modalities, such as video sequences and surgical tool data, provide important cues to estimate forces. A loss function designed to promote the modeling of smooth and sharp details found in force and torque signals results in a better force estimation quality. The modeling of forces due to pulling tasks is more challenging than for the simplest pushing actions. Abstract: 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, 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.