Towards Quantitative Endoscopy with Vision Intelligence

In this thesis, we work on topics related to quantitative endoscopy with vision-based intelligence. Specifically, our works revolve around the topic of video reconstruction in endoscopy, where many challenges exist, such as texture scarceness, illumination variation, multimodality, etc., and these prevent prior works from working effectively and robustly. To this end, we propose to combine the strength of expressivity of deep learning approaches and the rigorousness and accuracy of non-linear optimization algorithms to develop a series of methods to confront such challenges towards quantitative endoscopy. We first propose a retrospective sparse reconstruction method that can estimate a high-accuracy and density point cloud and high-completeness camera trajectory from a monocular endoscopic video with state-of-the-art performance. To enable this, replacing the role of a hand-crafted local descriptor, a deep image feature descriptor is developed to boost the feature matching performance in a typical sparse reconstruction algorithm. A retrospective surface reconstruction pipeline is then proposed to estimate a textured surface model from a monocular endoscopic video, where self-supervised depth and descriptor learning and surface fusion technique is involved. We show that the proposed method performs superior to a popular dense reconstruction method and the estimate reconstructions are in good agreement with the surface models obtained from CT scans. To align video-reconstructed surface models with pre-operative imaging such as CT, we introduce a global point cloud registration algorithm that is robust to resolution mismatch that often happens in such multi-modal scenarios. Specifically, a geometric feature descriptor is developed where a novel network normalization technique is used to help a 3D network produce more consistent and distinctive geometric features for samples with different resolutions. The proposed geometric descriptor achieves state-of-the-art performance, based on our evaluation. Last but not least, a real-time SLAM system that estimates a surface geometry and camera trajectory from a monocular endoscopic video is developed, where deep representations for geometry and appearance and non-linear factor graph optimization are used. We show that the proposed SLAM system performs favorably compared with a state-of-the-art feature-based SLAM system

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

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

The Human Auditory System

This book presents the latest findings in clinical audiology with a strong emphasis on new emerging technologies that facilitate and optimize a better assessment of the patient. The book has been edited with a strong educational perspective (all chapters include an introduction to their corresponding topic and a glossary of terms). The book contains material suitable for graduate students in audiology, ENT, hearing science and neuroscience

Endoscopy

Endoscopy is a fast moving field, and new techniques are continuously emerging. In recent decades, endoscopy has evolved and branched out from a diagnostic modality to enhanced video and computer assisting imaging with impressive interventional capabilities. The modern endoscopy has seen advances not only in types of endoscopes available, but also in types of interventions amenable to the endoscopic approach. To date, there are a lot more developments that are being trialed. Modern endoscopic equipment provides physicians with the benefit of many technical advances. Endoscopy is an effective and safe procedure even in special populations including pediatric patients and renal transplant patients. It serves as the tool for diagnosis and therapeutic interventions of many organs including gastrointestinal tract, head and neck, urinary tract and others

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

Validazione di un dispositivo indossabile basato sulla realta aumentata per il riposizionamento del mascellare superiore

Aim: We present a newly designed, localiser-free, head-mounted system featuring augmented reality (AR) as an aid to maxillofacial bone surgery, and assess the potential utility of the device by conducting a feasibility study and validation. Also, we implement a novel and ergonomic strategy designed to present AR information to the operating surgeon (hPnP). Methods: The head-mounted wearable system was developed as a stand- alone, video-based, see-through device in which the visual features were adapted to facilitate maxillofacial bone surgery. The system is designed to exhibit virtual planning overlaying the details of a real patient. We implemented a method allowing performance of waferless, AR-assisted maxillary repositioning. In vitro testing was conducted on a physical replica of a human skull. Surgical accuracy was measured. The outcomes were compared with those expected to be achievable in a three-dimensional environment. Data were derived using three levels of surgical planning, of increasing complexity, and for nine different operators with varying levels of surgical skill. Results: The mean linear error was 1.70±0.51mm. The axial errors were 0.89±0.54mm on the sagittal axis, 0.60±0.20mm on the frontal axis, and 1.06±0.40mm on the craniocaudal axis. Mean angular errors were also computed. Pitch: 3.13°±1.89°; Roll: 1.99°±0.95°; Yaw: 3.25°±2.26°. No significant difference in terms of error was noticed among operators, despite variations in surgical experience. Feedback from surgeons was acceptable; all tests were completed within 15 min and the tool was considered to be both comfortable and usable in practice. Conclusion: Our device appears to be accurate when used to assist in waferless maxillary repositioning. Our results suggest that the method can potentially be extended for use with many surgical procedures on the facial skeleton. Further, it would be appropriate to proceed to in vivo testing to assess surgical accuracy under real clinical conditions.Obiettivo: Presentare un nuovo sistema indossabile, privo di sistema di tracciamento esterno, che utilizzi la realtà aumentata come ausilio alla chirurgia ossea maxillo-facciale. Abbiamo validato il dispositivo. Inoltre, abbiamo implementato un nuovo metodo per presentare le informazioni aumentate al chirurgo (hPnP). Metodi: Le caratteristiche di visualizzazione del sistema, basato sul paradigma video see-through, sono state sviluppate specificamente per la chirurgia ossea maxillo-facciale. Il dispositivo è progettato per mostrare la pianificazione virtuale della chirurgia sovrapponendola all’anatomia del paziente. Abbiamo implementato un metodo che consente una tecnica senza splint, basata sulla realtà aumentata, per il riposizionamento del mascellare superiore. Il test in vitro è stato condotto su una replica di un cranio umano. La precisione chirurgica è stata misurata confrontando i risultati reali con quelli attesi. Il test è stato condotto utilizzando tre pianificazioni chirurgiche di crescente complessità, per nove operatori con diversi livelli di abilità chirurgica. Risultati: L'errore lineare medio è stato di 1,70±0,51mm. Gli errori assiali erano: 0,89±0,54mm sull'asse sagittale, 0,60±0,20mm sull'asse frontale, e 1,06±0,40mm sull'asse craniocaudale. Anche gli errori angolari medi sono stati calcolati. Beccheggio: 3.13°±1,89°; Rollio: 1,99°±0,95°; Imbardata: 3.25°±2,26°. Nessuna differenza significativa in termini di errore è stata rilevata tra gli operatori. Il feedback dei chirurghi è stato soddisfacente; tutti i test sono stati completati entro 15 minuti e lo strumento è stato considerato comodo e utilizzabile nella pratica. Conclusione: Il nostro dispositivo sembra essersi dimostrato preciso se utilizzato per eseguire il riposizionamento del mascellare superiore senza splint. I nostri risultati suggeriscono che il metodo può potenzialmente essere esteso ad altre procedure chirurgiche sullo scheletro facciale. Inoltre, appare utile procedere ai test in vivo per valutare la precisione chirurgica in condizioni cliniche reali

The Use of Predicates in FDA Regulation of Medical Devices: A Case Study of Robotic Surgical Devices

In the last decade, a number of high profile medical device recalls have drawn attention to the regulatory approval process, particularly the streamlined process for devices considered “lower risk” known as the 510(k). Approval of medical devices through the 510(k) Process is not based on clinical data, but rather on “substantial equivalence” to predicate devices approved pre-1976 or legally marketed thereafter. A predicate device is one that shares the same intended use as the new device and technological characteristics which are either the same or different without introducing new safety hazards. Many scholars believe that the premise of approving medical devices based on similarity to existing devices is inherently flawed. In particular, there is worry that presence of technology creep between predicate devices can lead to the approval of medical devices which ultimately do not resemble the original device for which clinical evidence exists, even as that evidence is used to validate device safety. Given these concerns about the safety of the established regulatory process, this thesis explored the impact of predicate creep within the 510(k) Process through a case study of a Robotic Assisted Surgery (RAS) devices, with particular focus on the Intuitive Surgical Da Vinci Surgical System. Through the development of new methodologies using publicly available data to measure predicate creep, this research traces the predicate ancestry of several RAS devices to assess the current impact and implications of predicate creep on the current regulatory process. The study concludes that there is significant evidence of predicate creep within the approval process and recommend new guidelines for classifying device risk and subsequent evidentiary requirements within the 510(k) Process, to reduce the number of devices with high levels of potential risk to public safety released onto the market

Robotic Assistant Systems for Otolaryngology-Head and Neck Surgery

Recently, there has been a significant movement in otolaryngology-head and neck surgery (OHNS) toward minimally invasive techniques, particularly those utilizing natural orifices. However, while these techniques can reduce the risk of complications encountered with classic open approaches such as scarring, infection, and damage to healthy tissue in order to access the surgical site, there remain significant challenges in both visualization and manipulation, including poor sensory feedback, reduced visibility, limited working area, and decreased precision due to long instruments. This work presents two robotic assistance systems which help to overcome different aspects of these challenges. The first is the Robotic Endo-Laryngeal Flexible (Robo-ELF) Scope, which assists surgeons in manipulating flexible endoscopes. Flexible endoscopes can provide superior visualization compared to microscopes or rigid endoscopes by allowing views not constrained by line-of-sight. However, they are seldom used in the operating room due to the difficulty in precisely manually manipulating and stabilizing them for long periods of time. The Robo-ELF Scope enables stable, precise robotic manipulation for flexible scopes and frees the surgeon’s hands to operate bimanually. The Robo-ELF Scope has been demonstrated and evaluated in human cadavers and is moving toward a human subjects study. The second is the Robotic Ear Nose and Throat Microsurgery System (REMS), which assists surgeons in manipulating rigid instruments and endoscopes. There are two main types of challenges involved in manipulating rigid instruments: reduced precision from hand tremor amplified by long instruments, and difficulty navigating through complex anatomy surrounded by sensitive structures. The REMS enables precise manipulation by allowing the surgeon to hold the surgical instrument while filtering unwanted movement such as hand tremor. The REMS also enables augmented navigation by calculating the position of the instrument with high accuracy, and combining this information with registered preoperative imaging data to enforce virtual safety barriers around sensitive anatomy. The REMS has been demonstrated and evaluated in user studies with synthetic phantoms and human cadavers