Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

Towards automated three-dimensional tracking of nephrons through stacked histological image sets

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of Witwatersrand for the degree of Master of Science in Engineering. August, 2015The three-dimensional microarchitecture of the mammalian kidney is of keen interest in the fields of cell biology and biomedical engineering as it plays a crucial role in renal function. This study presents a novel approach to the automatic tracking of individual nephrons through three-dimensional histological image sets of mouse and rat kidneys. The image database forms part of a previous study carried out at the University of Aarhus, Denmark. The previous study involved manually tracking a few hundred nephrons through the image sets in order to explore the renal microarchitecture, the results of which forms the gold standard for this study. The purpose of the current research is to develop methods which contribute towards creating an automated, intelligent system as a standard tool for such image sets. This would reduce the excessive time and human effort previously required for the tracking task, enabling a larger sample of nephrons to be tracked. It would also be desirable, in future, to explore the renal microstructure of various species and diseased specimens. The developed algorithm is robust, able to isolate closely packed nephrons and track their convoluted paths despite a number of non-ideal conditions such as local image distortions, artefacts and connective tissue interference. The system consists of initial image pre-processing steps such as background removal, adaptive histogram equalisation and image segmentation. A feature extraction stage achieves data abstraction and information concentration by extracting shape iii descriptors, radial shape profiles and key coordinates for each nephron crosssection. A custom graph-based tracking algorithm is implemented to track the nephrons using the extracted coordinates. A rule-base and machine learning algorithms including an Artificial Neural Network and Support Vector Machine are used to evaluate the shape features and other information to validate the algorithm’s results through each of its iterations. The validation steps prove to be highly effective in rejecting incorrect tracking moves, with the rule-base having greater than 90% accuracy and the Artificial Neural Network and Support Vector Machine both producing 93% classification accuracies. Comparison of a selection of automatically and manually tracked nephrons yielded results of 95% accuracy and 98% tracking extent for the proximal convoluted tubule, proximal straight tubule and ascending thick limb of the loop of Henle. The ascending and descending thin limbs of the loop of Henle pose a challenge, having low accuracy and low tracking extent due to the low resolution, narrow diameter and high density of cross-sections in the inner medulla. Limited manual intervention is proposed as a solution to these limitations, enabling full nephron paths to be obtained with an average of 17 manual corrections per mouse nephron and 58 manual corrections per rat nephron. The developed semi-automatic system saves a considerable amount of time and effort in comparison with the manual task. Furthermore, the developed methodology forms a foundation for future development towards a fully automated tracking system for nephrons

Skeletonization and segmentation of binary voxel shapes

Preface. This dissertation is the result of research that I conducted between January 2005 and December 2008 in the Visualization research group of the Technische Universiteit Eindhoven. I am pleased to have the opportunity to thank a number of people that made this work possible. I owe my sincere gratitude to Alexandru Telea, my supervisor and first promotor. I did not consider pursuing a PhD until my Master’s project, which he also supervised. Due to our pleasant collaboration from which I learned quite a lot, I became convinced that becoming a doctoral student would be the right thing to do for me. Indeed, I can say it has greatly increased my knowledge and professional skills. Alex, thank you for our interesting discussions and the freedom you gave me in conducting my research. You made these four years a pleasant experience. I am further grateful to Jack vanWijk, my second promotor. Our monthly discussions were insightful, and he continuously encouraged me to take a more formal and scientific stance. I would also like to thank Prof. Jan de Graaf from the department of mathematics for our discussions on some of my conjectures. His mathematical rigor was inspiring. I am greatly indebted to the Netherlands Organisation for Scientific Research (NWO) for funding my PhD project (grant number 612.065.414). I thank Prof. Kaleem Siddiqi, Prof. Mark de Berg, and Dr. Remco Veltkamp for taking part in the core doctoral committee and Prof. Deborah Silver and Prof. Jos Roerdink for participating in the extended committee. Our Visualization group provides a great atmosphere to do research in. In particular, I would like to thank my fellow doctoral students Frank van Ham, Hannes Pretorius, Lucian Voinea, Danny Holten, Koray Duhbaci, Yedendra Shrinivasan, Jing Li, NielsWillems, and Romain Bourqui. They enabled me to take my mind of research from time to time, by discussing political and economical affairs, and more trivial topics. Furthermore, I would like to thank the senior researchers of our group, Huub van de Wetering, Kees Huizing, and Michel Westenberg. In particular, I thank Andrei Jalba for our fruitful collaboration in the last part of my work. On a personal level, I would like to thank my parents and sister for their love and support over the years, my friends for providing distractions outside of the office, and Michelle for her unconditional love and ability to light up my mood when needed

Enhanced computer assisted detection of polyps in CT colonography

This thesis presents a novel technique for automatically detecting colorectal polyps in computed tomography colonography (CTC). The objective of the documented computer assisted diagnosis (CAD) technique is to deal with the issue of false positive detections without adversely affecting polyp detection sensitivity. The thesis begins with an overview of CTC and a review of the associated research areas, with particular attention given to CAD-CTC. This review identifies excessive false positive detections as a common problem associated with current CAD-CTC techniques. Addressing this problem constitutes the major contribution of this thesis. The documented CAD-CTC technique is trained with, and evaluated using, a series of clinical CTC data sets These data sets contain polyps with a range of different sizes and morphologies. The results presented m this thesis indicate the validity of the developed CAD-CTC technique and demonstrate its effectiveness m accurately detecting colorectal polyps while significantly reducing the number of false positive detections

A gaze-contingent framework for perceptually-enabled applications in healthcare

Patient safety and quality of care remain the focus of the smart operating room of the future. Some of the most influential factors with a detrimental effect are related to suboptimal communication among the staff, poor flow of information, staff workload and fatigue, ergonomics and sterility in the operating room. While technological developments constantly transform the operating room layout and the interaction between surgical staff and machinery, a vast array of opportunities arise for the design of systems and approaches, that can enhance patient safety and improve workflow and efficiency. The aim of this research is to develop a real-time gaze-contingent framework towards a "smart" operating suite, that will enhance operator's ergonomics by allowing perceptually-enabled, touchless and natural interaction with the environment. The main feature of the proposed framework is the ability to acquire and utilise the plethora of information provided by the human visual system to allow touchless interaction with medical devices in the operating room. In this thesis, a gaze-guided robotic scrub nurse, a gaze-controlled robotised flexible endoscope and a gaze-guided assistive robotic system are proposed. Firstly, the gaze-guided robotic scrub nurse is presented; surgical teams performed a simulated surgical task with the assistance of a robot scrub nurse, which complements the human scrub nurse in delivery of surgical instruments, following gaze selection by the surgeon. Then, the gaze-controlled robotised flexible endoscope is introduced; experienced endoscopists and novice users performed a simulated examination of the upper gastrointestinal tract using predominately their natural gaze. Finally, a gaze-guided assistive robotic system is presented, which aims to facilitate activities of daily living. The results of this work provide valuable insights into the feasibility of integrating the developed gaze-contingent framework into clinical practice without significant workflow disruptions.Open Acces

Semi-automated parallel programming in heterogeneous intelligent reconfigurable environments (SAPPHIRE)

In recent years, as we come closer to approaching physical limits in making smaller (and faster) computer processors, focus has instead been turned toward including multiple processor cores in each device. While this technically allows for more computational power as compared with only one traditional processor core, conventional software can typically only make use of a single processor. Furthermore, we see an increasing number of stream programs that process streams of data such as a stream of images or audio. For stream programs to effectively utilize multi-core processors, multithreading is the key, but it may be difficult to implement in practice depending on the complexity of the programs. We present SAPPHIRE: Semi-Automated Parallel Programming in Heterogeneous Intelligent Reconfigurable Environment, a middleware and SDK for developing multithreaded stream programs. In this middleware, we implement our semi-automated program construction technique which is designed to aid in writing multithreaded software by reducing needed complexity and lines of code written by software developers. We also present a novel static task-scheduling algorithm for stream programs with heterogeneous implementation choices. Our algorithm is capable of scheduling stream programs with provably near-optimal results given a specific set of assumptions, without requiring the unrolling of the task graph. Unrolling the task graph greatly increases the size of the input to the NP-Complete part of the task-scheduling problem as in related work. Finally, we present two case study programs implemented using SAPPHIRE. One case study, EM-Capture, has analyzed over 50 billion frames of endoscopy video in real-time in a real hospital, discerning over 71,000 unique endoscopy procedures. The other case study, EM-Feedback-RT, is a collaborative extension to EM-Capture, and is an attempt to provide real-time quality analysis feedback to physicians during a colonoscopy exam

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

On Visualizing Branched Surface: an Angle/Area Preserving Approach

The techniques of surface deformation and mapping are useful tools for the visualization of medical surfaces, especially for highly undulated or branched surfaces. In this thesis, two algorithms are presented for flattened visualizations of multi-branched medical surfaces, such as vessels. The first algorithm is an angle preserving approach, which is based on conformal analysis. The mapping function is obtained by minimizing two Dirichlet functionals. On a triangulated representation of vessel surfaces, this algorithm can be implemented efficiently using a finite element method. The second algorithm adjusts the result from conformal mapping to produce a flattened representation of the original surface while preserving areas. It employs the theory of optimal mass transport via a gradient descent approach. A new class of image morphing algorithms is also considered based on the theory of optimal mass transport. The mass moving energy functional is revised by adding an intensity penalizing term, in order to reduce the undesired "fading" effects. It is a parameter free approach. This technique has been applied on several natural and medical images to generate in-between image sequences.Ph.D.Allen Tannenbaum Committee Chair Anthony J. Yezzi, Committee Member; James Gruden, Committee Member; May D. Wang, Committee Member; Oskar Skrinjar, Committee Membe