Vision-Based Autonomous Control in Robotic Surgery

Robotic Surgery has completely changed surgical procedures. Enhanced dexterity, ergonomics, motion scaling, and tremor filtering, are well-known advantages introduced with respect to classical laparoscopy. In the past decade, robotic plays a fundamental role in Minimally Invasive Surgery (MIS) in which the da Vinci robotic system (Intuitive Surgical Inc., Sunnyvale, CA) is the most widely used system for robot-assisted laparoscopic procedures. Robots also have great potentiality in Microsurgical applications, where human limits are crucial and surgical sub-millimetric gestures could have enormous benefits with motion scaling and tremor compensation. However, surgical robots still lack advanced assistive control methods that could notably support surgeon's activity and perform surgical tasks in autonomy for a high quality of intervention. In this scenario, images are the main feedback the surgeon can use to correctly operate in the surgical site. Therefore, in view of the increasing autonomy in surgical robotics, vision-based techniques play an important role and can arise by extending computer vision algorithms to surgical scenarios. Moreover, many surgical tasks could benefit from the application of advanced control techniques, allowing the surgeon to work under less stressful conditions and performing the surgical procedures with more accuracy and safety. The thesis starts from these topics, providing surgical robots the ability to perform complex tasks helping the surgeon to skillfully manipulate the robotic system to accomplish the above requirements. An increase in safety and a reduction in mental workload is achieved through the introduction of active constraints, that can prevent the surgical tool from crossing a forbidden region and similarly generate constrained motion to guide the surgeon on a specific path, or to accomplish robotic autonomous tasks. This leads to the development of a vision-based method for robot-aided dissection procedure allowing the control algorithm to autonomously adapt to environmental changes during the surgical intervention using stereo images elaboration. Computer vision is exploited to define a surgical tools collision avoidance method that uses Forbidden Region Virtual Fixtures by rendering a repulsive force to the surgeon. Advanced control techniques based on an optimization approach are developed, allowing multiple tasks execution with task definition encoded through Control Barrier Functions (CBFs) and enhancing haptic-guided teleoperation system during suturing procedures. The proposed methods are tested on a different robotic platform involving da Vinci Research Kit robot (dVRK) and a new microsurgical robotic platform. Finally, the integration of new sensors and instruments in surgical robots are considered, including a multi-functional tool for dexterous tissues manipulation and different visual sensing technologies

임상술기 향상을 위한 딥러닝 기법 연구: 대장내시경 진단 및 로봇수술 술기 평가에 적용

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 의용생체공학전공, 2020. 8. 김희찬.This paper presents deep learning-based methods for improving performance of clinicians. Novel methods were applied to the following two clinical cases and the results were evaluated. In the first study, a deep learning-based polyp classification algorithm for improving clinical performance of endoscopist during colonoscopy diagnosis was developed. Colonoscopy is the main method for diagnosing adenomatous polyp, which can multiply into a colorectal cancer and hyperplastic polyps. The classification algorithm was developed using convolutional neural network (CNN), trained with colorectal polyp images taken by a narrow-band imaging colonoscopy. The proposed method is built around an automatic machine learning (AutoML) which searches for the optimal architecture of CNN for colorectal polyp image classification and trains the weights of the architecture. In addition, gradient-weighted class activation mapping technique was used to overlay the probabilistic basis of the prediction result on the polyp location to aid the endoscopists visually. To verify the improvement in diagnostic performance, the efficacy of endoscopists with varying proficiency levels were compared with or without the aid of the proposed polyp classification algorithm. The results confirmed that, on average, diagnostic accuracy was improved and diagnosis time was shortened in all proficiency groups significantly. In the second study, a surgical instruments tracking algorithm for robotic surgery video was developed, and a model for quantitatively evaluating the surgeons surgical skill based on the acquired motion information of the surgical instruments was proposed. The movement of surgical instruments is the main component of evaluation for surgical skill. Therefore, the focus of this study was develop an automatic surgical instruments tracking algorithm, and to overcome the limitations presented by previous methods. The instance segmentation framework was developed to solve the instrument occlusion issue, and a tracking framework composed of a tracker and a re-identification algorithm was developed to maintain the type of surgical instruments being tracked in the video. In addition, algorithms for detecting the tip position of instruments and arm-indicator were developed to acquire the movement of devices specialized for the robotic surgery video. The performance of the proposed method was evaluated by measuring the difference between the predicted tip position and the ground truth position of the instruments using root mean square error, area under the curve, and Pearsons correlation analysis. Furthermore, motion metrics were calculated from the movement of surgical instruments, and a machine learning-based robotic surgical skill evaluation model was developed based on these metrics. These models were used to evaluate clinicians, and results were similar in the developed evaluation models, the Objective Structured Assessment of Technical Skill (OSATS), and the Global Evaluative Assessment of Robotic Surgery (GEARS) evaluation methods. In this study, deep learning technology was applied to colorectal polyp images for a polyp classification, and to robotic surgery videos for surgical instruments tracking. The improvement in clinical performance with the aid of these methods were evaluated and verified.본 논문은 의료진의 임상술기 능력을 향상시키기 위하여 새로운 딥러닝 기법들을 제안하고 다음 두 가지 실례에 대해 적용하여 그 결과를 평가하였다. 첫 번째 연구에서는 대장내시경으로 광학 진단 시, 내시경 전문의의 진단 능력을 향상시키기 위하여 딥러닝 기반의 용종 분류 알고리즘을 개발하고, 내시경 전문의의 진단 능력 향상 여부를 검증하고자 하였다. 대장내시경 검사로 암종으로 증식할 수 있는 선종과 과증식성 용종을 진단하는 것은 중요하다. 본 연구에서는 협대역 영상 내시경으로 촬영한 대장 용종 영상으로 합성곱 신경망을 학습하여 분류 알고리즘을 개발하였다. 제안하는 알고리즘은 자동 기계학습 (AutoML) 방법으로, 대장 용종 영상에 최적화된 합성곱 신경망 구조를 찾고 신경망의 가중치를 학습하였다. 또한 기울기-가중치 클래스 활성화 맵핑 기법을 이용하여 개발한 합성곱 신경망 결과의 확률적 근거를 용종 위치에 시각적으로 나타나도록 함으로 내시경 전문의의 진단을 돕도록 하였다. 마지막으로, 숙련도 그룹별로 내시경 전문의가 용종 분류 알고리즘의 결과를 참고하였을 때 진단 능력이 향상되었는지 비교 실험을 진행하였고, 모든 그룹에서 유의미하게 진단 정확도가 향상되고 진단 시간이 단축되었음을 확인하였다. 두 번째 연구에서는 로봇수술 동영상에서 수술도구 위치 추적 알고리즘을 개발하고, 획득한 수술도구의 움직임 정보를 바탕으로 수술자의 숙련도를 정량적으로 평가하는 모델을 제안하였다. 수술도구의 움직임은 수술자의 로봇수술 숙련도를 평가하기 위한 주요한 정보이다. 따라서 본 연구는 딥러닝 기반의 자동 수술도구 추적 알고리즘을 개발하였으며, 다음 두가지 선행연구의 한계점을 극복하였다. 인스턴스 분할 (Instance Segmentation) 프레임웍을 개발하여 폐색 (Occlusion) 문제를 해결하였고, 추적기 (Tracker)와 재식별화 (Re-Identification) 알고리즘으로 구성된 추적 프레임웍을 개발하여 동영상에서 추적하는 수술도구의 종류가 유지되도록 하였다. 또한 로봇수술 동영상의 특수성을 고려하여 수술도구의 움직임을 획득하기위해 수술도구 끝 위치와 로봇 팔-인디케이터 (Arm-Indicator) 인식 알고리즘을 개발하였다. 제안하는 알고리즘의 성능은 예측한 수술도구 끝 위치와 정답 위치 간의 평균 제곱근 오차, 곡선 아래 면적, 피어슨 상관분석으로 평가하였다. 마지막으로, 수술도구의 움직임으로부터 움직임 지표를 계산하고 이를 바탕으로 기계학습 기반의 로봇수술 숙련도 평가 모델을 개발하였다. 개발한 평가 모델은 기존의 Objective Structured Assessment of Technical Skill (OSATS), Global Evaluative Assessment of Robotic Surgery (GEARS) 평가 방법과 유사한 성능을 보임을 확인하였다. 본 논문은 의료진의 임상술기 능력을 향상시키기 위하여 대장 용종 영상과 로봇수술 동영상에 딥러닝 기술을 적용하고 그 유효성을 확인하였으며, 향후에 제안하는 방법이 임상에서 사용되고 있는 진단 및 평가 방법의 대안이 될 것으로 기대한다.Chapter 1 General Introduction 1 1.1 Deep Learning for Medical Image Analysis 1 1.2 Deep Learning for Colonoscipic Diagnosis 2 1.3 Deep Learning for Robotic Surgical Skill Assessment 3 1.4 Thesis Objectives 5 Chapter 2 Optical Diagnosis of Colorectal Polyps using Deep Learning with Visual Explanations 7 2.1 Introduction 7 2.1.1 Background 7 2.1.2 Needs 8 2.1.3 Related Work 9 2.2 Methods 11 2.2.1 Study Design 11 2.2.2 Dataset 14 2.2.3 Preprocessing 17 2.2.4 Convolutional Neural Networks (CNN) 21 2.2.4.1 Standard CNN 21 2.2.4.2 Search for CNN Architecture 22 2.2.4.3 Searched CNN Training 23 2.2.4.4 Visual Explanation 24 2.2.5 Evaluation of CNN and Endoscopist Performances 25 2.3 Experiments and Results 27 2.3.1 CNN Performance 27 2.3.2 Results of Visual Explanation 31 2.3.3 Endoscopist with CNN Performance 33 2.4 Discussion 45 2.4.1 Research Significance 45 2.4.2 Limitations 47 2.5 Conclusion 49 Chapter 3 Surgical Skill Assessment during Robotic Surgery by Deep Learning-based Surgical Instrument Tracking 50 3.1 Introduction 50 3.1.1 Background 50 3.1.2 Needs 51 3.1.3 Related Work 52 3.2 Methods 56 3.2.1 Study Design 56 3.2.2 Dataset 59 3.2.3 Instance Segmentation Framework 63 3.2.4 Tracking Framework 66 3.2.4.1 Tracker 66 3.2.4.2 Re-identification 68 3.2.5 Surgical Instrument Tip Detection 69 3.2.6 Arm-Indicator Recognition 71 3.2.7 Surgical Skill Prediction Model 71 3.3 Experiments and Results 78 3.3.1 Performance of Instance Segmentation Framework 78 3.3.2 Performance of Tracking Framework 82 3.3.3 Evaluation of Surgical Instruments Trajectory 83 3.3.4 Evaluation of Surgical Skill Prediction Model 86 3.4 Discussion 90 3.4.1 Research Significance 90 3.4.2 Limitations 92 3.5 Conclusion 96 Chapter 4 Summary and Future Works 97 4.1 Thesis Summary 97 4.2 Limitations and Future Works 98 Bibliography 100 Abstract in Korean 116 Acknowledgement 119Docto

Dynamic Active Constraints for Surgical Robots using Vector Field Inequalities

Robotic assistance allows surgeons to perform dexterous and tremor-free procedures, but robotic aid is still underrepresented in procedures with constrained workspaces, such as deep brain neurosurgery and endonasal surgery. In these procedures, surgeons have restricted vision to areas near the surgical tooltips, which increases the risk of unexpected collisions between the shafts of the instruments and their surroundings. In this work, our vector-field-inequalities method is extended to provide dynamic active-constraints to any number of robots and moving objects sharing the same workspace. The method is evaluated with experiments and simulations in which robot tools have to avoid collisions autonomously and in real-time, in a constrained endonasal surgical environment. Simulations show that with our method the combined trajectory error of two robotic systems is optimal. Experiments using a real robotic system show that the method can autonomously prevent collisions between the moving robots themselves and between the robots and the environment. Moreover, the framework is also successfully verified under teleoperation with tool-tissue interactions.Comment: Accepted on T-RO 2019, 19 Page

Kinematic analysis of a novel 2-d.o.f. orientation device

This paper presents the development of a new parallel robot designed for helping with bone milling surgeries. The robot is a small modular wrist with 2 active degrees of freedom, and it is proposed to be used as an orientation device located at the end of a robotic arm designed for bone milling processes. A generic kinematic geometry is proposed for this device. This first article shows the developments on the workspace optimization and the analysis of the force field required to complete a reconstruction of the inferior jawbone. The singularities of the mechanism are analyzed, and the actuator selection is justified with the torque requirements and the study of the force space. The results obtained by the simulations allow building a first prototype using linear motors. Bone milling experiment video is shown as additional material

Toward Force Estimation in Robot-Assisted Surgery using Deep Learning with Vision and Robot State

Knowledge of interaction forces during teleoperated robot-assisted surgery could be used to enable force feedback to human operators and evaluate tissue handling skill. However, direct force sensing at the end-effector is challenging because it requires biocompatible, sterilizable, and cost-effective sensors. Vision-based deep learning using convolutional neural networks is a promising approach for providing useful force estimates, though questions remain about generalization to new scenarios and real-time inference. We present a force estimation neural network that uses RGB images and robot state as inputs. Using a self-collected dataset, we compared the network to variants that included only a single input type, and evaluated how they generalized to new viewpoints, workspace positions, materials, and tools. We found that vision-based networks were sensitive to shifts in viewpoints, while state-only networks were robust to changes in workspace. The network with both state and vision inputs had the highest accuracy for an unseen tool, and was moderately robust to changes in viewpoints. Through feature removal studies, we found that using only position features produced better accuracy than using only force features as input. The network with both state and vision inputs outperformed a physics-based baseline model in accuracy. It showed comparable accuracy but faster computation times than a baseline recurrent neural network, making it better suited for real-time applications.Comment: 7 pages, 6 figures, submitted to ICRA 202

Gesture Recognition and Control for Semi-Autonomous Robotic Assistant Surgeons

The next stage for robotics development is to introduce autonomy and cooperation with human agents in tasks that require high levels of precision and/or that exert considerable physical strain. To guarantee the highest possible safety standards, the best approach is to devise a deterministic automaton that performs identically for each operation. Clearly, such approach inevitably fails to adapt itself to changing environments or different human companions. In a surgical scenario, the highest variability happens for the timing of different actions performed within the same phases. This thesis explores the solutions adopted in pursuing automation in robotic minimally-invasive surgeries (R-MIS) and presents a novel cognitive control architecture that uses a multi-modal neural network trained on a cooperative task performed by human surgeons and produces an action segmentation that provides the required timing for actions while maintaining full phase execution control via a deterministic Supervisory Controller and full execution safety by a velocity-constrained Model-Predictive Controller

Medical SLAM in an autonomous robotic system

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects

Medical SLAM in an autonomous robotic system

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects

Cable-driven parallel mechanisms for minimally invasive robotic surgery

Minimally invasive surgery (MIS) has revolutionised surgery by providing faster recovery times, less post-operative complications, improved cosmesis and reduced pain for the patient. Surgical robotics are used to further decrease the invasiveness of procedures, by using yet smaller and fewer incisions or using natural orifices as entry point. However, many robotic systems still suffer from technical challenges such as sufficient instrument dexterity and payloads, leading to limited adoption in clinical practice. Cable-driven parallel mechanisms (CDPMs) have unique properties, which can be used to overcome existing challenges in surgical robotics. These beneficial properties include high end-effector payloads, efficient force transmission and a large configurable instrument workspace. However, the use of CDPMs in MIS is largely unexplored. This research presents the first structured exploration of CDPMs for MIS and demonstrates the potential of this type of mechanism through the development of multiple prototypes: the ESD CYCLOPS, CDAQS, SIMPLE, neuroCYCLOPS and microCYCLOPS. One key challenge for MIS is the access method used to introduce CDPMs into the body. Three different access methods are presented by the prototypes. By focusing on the minimally invasive access method in which CDPMs are introduced into the body, the thesis provides a framework, which can be used by researchers, engineers and clinicians to identify future opportunities of CDPMs in MIS. Additionally, through user studies and pre-clinical studies, these prototypes demonstrate that this type of mechanism has several key advantages for surgical applications in which haptic feedback, safe automation or a high payload are required. These advantages, combined with the different access methods, demonstrate that CDPMs can have a key role in the advancement of MIS technology.Open Acces