Enhancing endoscopic navigation and polyp detection using artificial intelligence

Colorectal cancer (CRC) is one most common and deadly forms of cancer. It has a very high mortality rate if the disease advances to late stages however early diagnosis and treatment can be curative is hence essential to enhancing disease management. Colonoscopy is considered the gold standard for CRC screening and early therapeutic treatment. The effectiveness of colonoscopy is highly dependent on the operator’s skill, as a high level of hand-eye coordination is required to control the endoscope and fully examine the colon wall. Because of this, detection rates can vary between different gastroenterologists and technology have been proposed as solutions to assist disease detection and standardise detection rates. This thesis focuses on developing artificial intelligence algorithms to assist gastroenterologists during colonoscopy with the potential to ensure a baseline standard of quality in CRC screening. To achieve such assistance, the technical contributions develop deep learning methods and architectures for automated endoscopic image analysis to address both the detection of lesions in the endoscopic image and the 3D mapping of the endoluminal environment. The proposed detection models can run in real-time and assist visualization of different polyp types. Meanwhile the 3D reconstruction and mapping models developed are the basis for ensuring that the entire colon has been examined appropriately and to support quantitative measurement of polyp sizes using the image during a procedure. Results and validation studies presented within the thesis demonstrate how the developed algorithms perform on both general scenes and on clinical data. The feasibility of clinical translation is demonstrated for all of the models on endoscopic data from human participants during CRC screening examinations

Deep Causal Learning for Robotic Intelligence

This invited review discusses causal learning in the context of robotic intelligence. The paper introduced the psychological findings on causal learning in human cognition, then it introduced the traditional statistical solutions on causal discovery and causal inference. The paper reviewed recent deep causal learning algorithms with a focus on their architectures and the benefits of using deep nets and discussed the gap between deep causal learning and the needs of robotic intelligence

Vision-based retargeting for endoscopic navigation

Endoscopy is a standard procedure for visualising the human gastrointestinal tract. With the advances in biophotonics, imaging techniques such as narrow band imaging, confocal laser endomicroscopy, and optical coherence tomography can be combined with normal endoscopy for assisting the early diagnosis of diseases, such as cancer. In the past decade, optical biopsy has emerged to be an effective tool for tissue analysis, allowing in vivo and in situ assessment of pathological sites with real-time feature-enhanced microscopic images. However, the non-invasive nature of optical biopsy leads to an intra-examination retargeting problem, which is associated with the difficulty of re-localising a biopsied site consistently throughout the whole examination. In addition to intra-examination retargeting, retargeting of a pathological site is even more challenging across examinations, due to tissue deformation and changing tissue morphologies and appearances. The purpose of this thesis is to address both the intra- and inter-examination retargeting problems associated with optical biopsy. We propose a novel vision-based framework for intra-examination retargeting. The proposed framework is based on combining visual tracking and detection with online learning of the appearance of the biopsied site. Furthermore, a novel cascaded detection approach based on random forests and structured support vector machines is developed to achieve efficient retargeting. To cater for reliable inter-examination retargeting, the solution provided in this thesis is achieved by solving an image retrieval problem, for which an online scene association approach is proposed to summarise an endoscopic video collected in the first examination into distinctive scenes. A hashing-based approach is then used to learn the intrinsic representations of these scenes, such that retargeting can be achieved in subsequent examinations by retrieving the relevant images using the learnt representations. For performance evaluation of the proposed frameworks, extensive phantom, ex vivo and in vivo experiments have been conducted, with results demonstrating the robustness and potential clinical values of the methods proposed.Open Acces

Smart Camera Robotic Assistant for Laparoscopic Surgery

The cognitive architecture also includes learning mechanisms to adapt the behavior of the robot to the different ways of working of surgeons, and to improve the robot behavior through experience, in a similar way as a human assistant would do. The theoretical concepts of this dissertation have been validated both through in-vitro experimentation in the labs of medical robotics of the University of Malaga and through in-vivo experimentation with pigs in the IACE Center (Instituto Andaluz de Cirugía Experimental), performed by expert surgeons.In the last decades, laparoscopic surgery has become a daily practice in operating rooms worldwide, which evolution is tending towards less invasive techniques. In this scenario, robotics has found a wide field of application, from slave robotic systems that replicate the movements of the surgeon to autonomous robots able to assist the surgeon in certain maneuvers or to perform autonomous surgical tasks. However, these systems require the direct supervision of the surgeon, and its capacity of making decisions and adapting to dynamic environments is very limited. This PhD dissertation presents the design and implementation of a smart camera robotic assistant to collaborate with the surgeon in a real surgical environment. First, it presents the design of a novel camera robotic assistant able to augment the capacities of current vision systems. This robotic assistant is based on an intra-abdominal camera robot, which is completely inserted into the patient’s abdomen and it can be freely moved along the abdominal cavity by means of magnetic interaction with an external magnet. To provide the camera with the autonomy of motion, the external magnet is coupled to the end effector of a robotic arm, which controls the shift of the camera robot along the abdominal wall. This way, the robotic assistant proposed in this dissertation has six degrees of freedom, which allow providing a wider field of view compared to the traditional vision systems, and also to have different perspectives of the operating area. On the other hand, the intelligence of the system is based on a cognitive architecture specially designed for autonomous collaboration with the surgeon in real surgical environments. The proposed architecture simulates the behavior of a human assistant, with a natural and intuitive human-robot interface for the communication between the robot and the surgeon

Interfaces for Modular Surgical Planning and Assistance Systems

Modern surgery of the 21st century relies in many aspects on computers or, in a wider sense, digital data processing. Department administration, OR scheduling, billing, and - with increasing pervasion - patient data management are performed with the aid of so called Surgical Information Systems (SIS) or, more general, Hospital Information Systems (HIS). Computer Assisted Surgery (CAS) summarizes techniques which assist a surgeon in the preparation and conduction of surgical interventions. Today still predominantly based on radiology images, these techniques include the preoperative determination of an optimal surgical strategy and intraoperative systems which aim at increasing the accuracy of surgical manipulations. CAS is a relatively young field of computer science. One of the unsolved "teething troubles" of CAS is the absence of technical standards for the interconnectivity of CAS system. Current CAS systems are usually "islands of information" with no connection to other devices within the operating room or hospital-wide information systems. Several workshop reports and individual publications point out that this situation leads to ergonomic, logistic, and economic limitations in hospital work. Perioperative processes are prolonged by the manual installation and configuration of an increasing amount of technical devices. Intraoperatively, a large amount of the surgeons'' attention is absorbed by the requirement to monitor and operate systems. The need for open infrastructures which enable the integration of CAS devices from different vendors in order to exchange information as well as commands among these devices through a network has been identified by numerous experts with backgrounds in medicine as well as engineering. This thesis contains two approaches to the integration of CAS systems: - For perioperative data exchange, the specification of new data structures as an amendment to the existing DICOM standard for radiology image management is presented. The extension of DICOM towards surgical application allows for the seamless integration of surgical planning and reporting systems into DICOM-based Picture Archiving and Communication Systems (PACS) as they are installed in most hospitals for the exchange and long-term archival of patient images and image-related patient data. - For the integration of intraoperatively used CAS devices, such as, e.g., navigation systems, video image sources, or biosensors, the concept of a surgical middleware is presented. A c++ class library, the TiCoLi, is presented which facilitates the configuration of ad-hoc networks among the modules of a distributed CAS system as well as the exchange of data streams, singular data objects, and commands between these modules. The TiCoLi is the first software library for a surgical field of application to implement all of these services. To demonstrate the suitability of the presented specifications and their implementation, two modular CAS applications are presented which utilize the proposed DICOM extensions for perioperative exchange of surgical planning data as well as the TiCoLi for establishing an intraoperative network of autonomous, yet not independent, CAS modules.Die moderne Hochleistungschirurgie des 21. Jahrhunderts ist auf vielerlei Weise abhängig von Computern oder, im weiteren Sinne, der digitalen Datenverarbeitung. Administrative Abläufe, wie die Erstellung von Nutzungsplänen für die verfügbaren technischen, räumlichen und personellen Ressourcen, die Rechnungsstellung und - in zunehmendem Maße - die Verwaltung und Archivierung von Patientendaten werden mit Hilfe von digitalen Informationssystemen rationell und effizient durchgeführt. Innerhalb der Krankenhausinformationssysteme (KIS, oder englisch HIS) stehen für die speziellen Bedürfnisse der einzelnen Fachabteilungen oft spezifische Informationssysteme zur Verfügung. Chirurgieinformationssysteme (CIS, oder englisch SIS) decken hierbei vor allen Dingen die Bereiche Operationsplanung sowie Materialwirtschaft für spezifisch chirurgische Verbrauchsmaterialien ab. Während die genannten HIS und SIS vornehmlich der Optimierung administrativer Aufgaben dienen, stehen die Systeme der Computerassistierten Chirugie (CAS) wesentlich direkter im Dienste der eigentlichen chirugischen Behandlungsplanung und Therapie. Die CAS verwendet Methoden der Robotik, digitalen Bild- und Signalverarbeitung, künstlichen Intelligenz, numerischen Simulation, um nur einige zu nennen, zur patientenspezifischen Behandlungsplanung und zur intraoperativen Unterstützung des OP-Teams, allen voran des Chirurgen. Vor allen Dingen Fortschritte in der räumlichen Verfolgung von Werkzeugen und Patienten ("Tracking"), die Verfügbarkeit dreidimensionaler radiologischer Aufnahmen (CT, MRT, ...) und der Einsatz verschiedener Robotersysteme haben in den vergangenen Jahrzehnten den Einzug des Computers in den Operationssaal - medienwirksam - ermöglicht. Weniger prominent, jedoch keinesfalls von untergeordnetem praktischen Nutzen, sind Beispiele zur automatisierten Überwachung klinischer Messwerte, wie etwa Blutdruck oder Sauerstoffsättigung. Im Gegensatz zu den meist hochgradig verteilten und gut miteinander verwobenen Informationssystemen für die Krankenhausadministration und Patientendatenverwaltung, sind die Systeme der CAS heutzutage meist wenig oder überhaupt nicht miteinander und mit Hintergrundsdatenspeichern vernetzt. Eine Reihe wissenschaftlicher Publikationen und interdisziplinärer Workshops hat sich in den vergangen ein bis zwei Jahrzehnten mit den Problemen des Alltagseinsatzes von CAS Systemen befasst. Mit steigender Intensität wurde hierbei auf den Mangel an infrastrukturiellen Grundlagen für die Vernetzung intraoperativ eingesetzter CAS Systeme miteinander und mit den perioperativ eingesetzten Planungs-, Dokumentations- und Archivierungssystemen hingewiesen. Die sich daraus ergebenden negativen Einflüsse auf die Effizienz perioperativer Abläufe - jedes Gerät muss manuell in Betrieb genommen und mit den spezifischen Daten des nächsten Patienten gefüttert werden - sowie die zunehmende Aufmerksamkeit, welche der Operateur und sein Team auf die Überwachung und dem Betrieb der einzelnen Geräte verwenden muss, werden als eine der "Kinderkrankheiten" dieser relativ jungen Technologie betrachtet und stehen einer Verbreitung über die Grenzen einer engagierten technophilen Nutzergruppe hinaus im Wege. Die vorliegende Arbeit zeigt zwei parallel von einander (jedoch, im Sinne der Schnittstellenkompatibilität, nicht gänzlich unabhängig voneinander) zu betreibende Ansätze zur Integration von CAS Systemen. - Für den perioperativen Datenaustausch wird die Spezifikation zusätzlicher Datenstrukturen zum Transfer chirurgischer Planungsdaten im Rahmen des in radiologischen Bildverarbeitungssystemen weit verbreiteten DICOM Standards vorgeschlagen und an zwei Beispielen vorgeführt. Die Erweiterung des DICOM Standards für den perioperativen Einsatz ermöglicht hierbei die nahtlose Integration chirurgischer Planungssysteme in existierende "Picture Archiving and Communication Systems" (PACS), welche in den meisten Fällen auf dem DICOM Standard basieren oder zumindest damit kompatibel sind. Dadurch ist einerseits der Tatsache Rechnung getragen, dass die patientenspezifische OP-Planung in hohem Masse auf radiologischen Bildern basiert und andererseits sicher gestellt, dass die Planungsergebnisse entsprechend der geltenden Bestimmungen langfristig archiviert und gegen unbefugten Zugriff geschützt sind - PACS Server liefern hier bereits wohlerprobte Lösungen. - Für die integration intraoperativer CAS Systeme, wie etwa Navigationssysteme, Videobildquellen oder Sensoren zur Überwachung der Vitalparameter, wird das Konzept einer "chirurgischen Middleware" vorgestellt. Unter dem Namen TiCoLi wurde eine c++ Klassenbibliothek entwickelt, auf deren Grundlage die Konfiguration von ad-hoc Netzwerken während der OP-Vorbereitung mittels plug-and-play Mechanismen erleichtert wird. Nach erfolgter Konfiguration ermöglicht die TiCoLi den Austausch kontinuierlicher Datenströme sowie einzelner Datenpakete und Kommandos zwischen den Modulen einer verteilten CAS Anwendung durch ein Ethernet-basiertes Netzwerk. Die TiCoLi ist die erste frei verfügbare Klassenbibliothek welche diese Funktionalitäten dediziert für einen Einsatz im chirurgischen Umfeld vereinigt. Zum Nachweis der Tauglichkeit der gezeigten Spezifikationen und deren Implementierungen, werden zwei modulare CAS Anwendungen präsentiert, welche die vorgeschlagenen DICOM Erweiterungen zum perioperativen Austausch von Planungsergebnissen sowie die TiCoLi zum intraoperativen Datenaustausch von Messdaten unter echzeitnahen Anforderungen verwenden

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

InterNAV3D: A Navigation Tool for Robot-Assisted Needle-Based Intervention for the Lung

Lung cancer is one of the leading causes of cancer deaths in North America. There are recent advances in cancer treatment techniques that can treat cancerous tumors, but require a real-time imaging modality to provide intraoperative assistive feedback. Ultrasound (US) imaging is one such modality. However, while its application to the lungs has been limited because of the deterioration of US image quality (due to the presence of air in the lungs); recent work has shown that appropriate lung deflation can help to improve the quality sufficiently to enable intraoperative, US-guided robotics-assisted techniques to be used. The work described in this thesis focuses on this approach. The thesis describes a project undertaken at Canadian Surgical Technologies and Advanced Robotics (CSTAR) that utilizes the image processing techniques to further enhance US images and implements an advanced 3D virtual visualization software approach. The application considered is that for minimally invasive lung cancer treatment using procedures such as brachytherapy and microwave ablation while taking advantage of the accuracy and teleoperation capabilities of surgical robots, to gain higher dexterity and precise control over the therapy tools (needles and probes). A number of modules and widgets are developed and explained which improve the visibility of the physical features of interest in the treatment and help the clinician to have more reliable and accurate control of the treatment. Finally the developed tools are validated with extensive experimental evaluations and future developments are suggested to enhance the scope of the applications