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

Surgical Data Science - from Concepts toward Clinical Translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process

Medical Robotics

The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not

A Survey on the Current Status and Future Challenges Towards Objective Skills Assessment in Endovascular Surgery

Minimally-invasive endovascular interventions have evolved rapidly over the past decade, facilitated by breakthroughs in medical imaging and sensing, instrumentation and most recently robotics. Catheter based operations are potentially safer and applicable to a wider patient population due to the reduced comorbidity. As a result endovascular surgery has become the preferred treatment option for conditions previously treated with open surgery and as such the number of patients undergoing endovascular interventions is increasing every year. This fact coupled with a proclivity for reduced working hours, results in a requirement for efficient training and assessment of new surgeons, that deviates from the “see one, do one, teach one” model introduced by William Halsted, so that trainees obtain operational expertise in a shorter period. Developing more objective assessment tools based on quantitative metrics is now a recognised need in interventional training and this manuscript reports the current literature for endovascular skills assessment and the associated emerging technologies. A systematic search was performed on PubMed (MEDLINE), Google Scholar, IEEXplore and known journals using the keywords, “endovascular surgery”, “surgical skills”, “endovascular skills”, “surgical training endovascular” and “catheter skills”. Focusing explicitly on endovascular surgical skills, we group related works into three categories based on the metrics used; structured scales and checklists, simulation-based and motion-based metrics. This review highlights the key findings in each category and also provides suggestions for new research opportunities towards fully objective and automated surgical assessment solutions

Intelligent computing applications to assist perceptual training in medical imaging

The research presented in this thesis represents a body of work which addresses issues in medical imaging, primarily as it applies to breast cancer screening and laparoscopic surgery. The concern here is how computer based methods can aid medical practitioners in these tasks. Thus, research is presented which develops both new techniques of analysing radiologists performance data and also new approaches of examining surgeons visual behaviour when they are undertaking laparoscopic training. Initially a new chest X-Ray self-assessment application is described which has been developed to assess and improve radiologists performance in detecting lung cancer. Then, in breast cancer screening, a method of identifying potential poor performance outliers at an early stage in a national self-assessment scheme is demonstrated. Additionally, a method is presented to optimize whether a radiologist, in using this scheme, has correctly localised and identified an abnormality or made an error. One issue in appropriately measuring radiological performance in breast screening is that both the size of clinical monitors used and the difficulty in linking the medical image to the observer s line of sight hinders suitable eye tracking. Consequently, a new method is presented which links these two items. Laparoscopic surgeons have similar issues to radiologists in interpreting a medical display but with the added complications of hand-eye co-ordination. Work is presented which examines whether visual search feedback of surgeons operations can be useful training aids

Digital Pathology: The Time Is Now to Bridge the Gap between Medicine and Technological Singularity

Digitalization of the imaging in radiology is a reality in several healthcare institutions worldwide. The challenges of filing, confidentiality, and manipulation have been brilliantly solved in radiology. However, digitalization of hematoxylin- and eosin-stained routine histological slides has shown slow movement. Although the application for external quality assurance is a reality for a pathologist with most of the continuing medical education programs utilizing virtual microscopy, the abandonment of traditional glass slides for routine diagnostics is far from the perspectives of many departments of laboratory medicine and pathology. Digital pathology images are captured as images by scanning and whole slide imaging/virtual microscopy can be obtained by microscopy (robotic) on an entire histological (microscopic) glass slide. Since 1986, services using telepathology for the transfer of images of anatomic pathology between detached locations have benefited countless patients globally, including the University of Alberta. The purpose of specialist recertification or re-validation for the Royal College of Pathologists of Canada belonging to the Royal College of Physicians and Surgeons of Canada and College of American Pathologists is a milestone in virtual reality. Challenges, such as high bandwidth requirement, electronic platforms, the stability of the operating systems, have been targeted and are improving enormously. The encryption of digital images may be a requirement for the accreditation of laboratory services—quantum computing results in quantum-mechanical phenomena, such as superposition and entanglement. Different from binary digital electronic computers based on transistors where data are encoded into binary digits (bits) with two different states (0 and 1), quantum computing uses quantum bits (qubits), which can be in superpositions of states. The use of quantum computing protocols on encrypted data is crucial for the permanent implementation of virtual pathology in hospitals and universities. Quantum computing may well represent the technological singularity to create new classifications and taxonomic rules in medicine

Towards safety in minimally invasive surgery : patient safety, tissue handling and training aspects

In recent years a flow of media reports about unsafe situations in operating rooms have reached the general public. Awareness of the importance of patient safety also reached politicians. The report by the Dutch inspectorate of health care __Risico__s minimaal invasieve chirurgie onderschat__ (Risks minimally invasive surgery underestimated) stressed that patient safety is especially at risk in Minimally Invasive Surgery. Therefore patient safety became a focus of research and quality improvement, also in minimally invasive surgery. The current thesis aimes to give insight into patient safety risk factors in minimally invasive surgery. Of all examined risk factors minimally invasive surgical skills appeared to be directly related to patient safety. Therefore special focus for training of these skills is necessary. Previous research has shown that during simulation training objective assessment of economy of movements and time is possible. However, until recently there was no way to objectively assess one of the most important surgical skills: tissue handling. The development of a force sensor has made it possible to measure interaction forces with artificial tissue. In this thesis the clinical implications of a force sensor and the need of this new technology within training of minimally invasive surgical skills is examined.Financial support for the publication of this thesis was kindly provided by: Nederlandse Vereniging voor Endoscopische Chirurgie, Medical Dynamics, Olympus Nederland BV, ERBE Nederland BV, Skills Meducation, ChipSoft, Memidis Pharma b.v., Goodlife Healthcare bv, Covidien, Stöpler Instrumenten en Apparaten B.V.UBL - phd migration 201

Data-driven resiliency assessment of medical cyber-physical systems

Advances in computing, networking, and sensing technologies have resulted in the ubiquitous deployment of medical cyber-physical systems in various clinical and personalized settings. The increasing complexity and connectivity of such systems, the tight coupling between their cyber and physical components, and the inevitable involvement of human operators in supervision and control have introduced major challenges in ensuring system reliability, safety, and security. This dissertation takes a data-driven approach to resiliency assessment of medical cyber-physical systems. Driven by large-scale studies of real safety incidents involving medical devices, we develop techniques and tools for (i) deeper understanding of incident causes and measurement of their impacts, (ii) validation of system safety mechanisms in the presence of realistic hazard scenarios, and (iii) preemptive real-time detection of safety hazards to mitigate adverse impacts on patients. We present a framework for automated analysis of structured and unstructured data from public FDA databases on medical device recalls and adverse events. This framework allows characterization of the safety issues originated from computer failures in terms of fault classes, failure modes, and recovery actions. We develop an approach for constructing ontology models that enable automated extraction of safety-related features from unstructured text. The proposed ontology model is defined based on device-specific human-in-the-loop control structures in order to facilitate the systems-theoretic causality analysis of adverse events. Our large-scale analysis of FDA data shows that medical devices are often recalled because of failure to identify all potential safety hazards, use of safety mechanisms that have not been rigorously validated, and limited capability in real-time detection and automated mitigation of hazards. To address those problems, we develop a safety hazard injection framework for experimental validation of safety mechanisms in the presence of accidental failures and malicious attacks. To reduce the test space for safety validation, this framework uses systems-theoretic accident causality models in order to identify the critical locations within the system to target software fault injection. For mitigation of safety hazards at run time, we present a model-based analysis framework that estimates the consequences of control commands sent from the software to the physical system through real-time computation of the system’s dynamics, and preemptively detects if a command is unsafe before its adverse consequences manifest in the physical system. The proposed techniques are evaluated on a real-world cyber-physical system for robot-assisted minimally invasive surgery and are shown to be more effective than existing methods in identifying system vulnerabilities and deficiencies in safety mechanisms as well as in preemptive detection of safety hazards caused by malicious attacks