4 research outputs found
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
A continuum robotic platform for endoscopic non-contact laser surgery: design, control, and preclinical evaluation
The application of laser technologies in surgical interventions has been accepted in the clinical
domain due to their atraumatic properties. In addition to manual application of fibre-guided
lasers with tissue contact, non-contact transoral laser microsurgery (TLM) of laryngeal tumours
has been prevailed in ENT surgery. However, TLM requires many years of surgical training
for tumour resection in order to preserve the function of adjacent organs and thus preserve the
patient’s quality of life. The positioning of the microscopic laser applicator outside the patient
can also impede a direct line-of-sight to the target area due to anatomical variability and limit
the working space. Further clinical challenges include positioning the laser focus on the tissue
surface, imaging, planning and performing laser ablation, and motion of the target area during
surgery. This dissertation aims to address the limitations of TLM through robotic approaches and
intraoperative assistance. Although a trend towards minimally invasive surgery is apparent, no
highly integrated platform for endoscopic delivery of focused laser radiation is available to date.
Likewise, there are no known devices that incorporate scene information from endoscopic imaging
into ablation planning and execution. For focusing of the laser beam close to the target tissue, this
work first presents miniaturised focusing optics that can be integrated into endoscopic systems.
Experimental trials characterise the optical properties and the ablation performance. A robotic
platform is realised for manipulation of the focusing optics. This is based on a variable-length
continuum manipulator. The latter enables movements of the endoscopic end effector in five
degrees of freedom with a mechatronic actuation unit. The kinematic modelling and control of the
robot are integrated into a modular framework that is evaluated experimentally. The manipulation
of focused laser radiation also requires precise adjustment of the focal position on the tissue. For
this purpose, visual, haptic and visual-haptic assistance functions are presented. These support
the operator during teleoperation to set an optimal working distance. Advantages of visual-haptic
assistance are demonstrated in a user study. The system performance and usability of the overall
robotic system are assessed in an additional user study. Analogous to a clinical scenario, the
subjects follow predefined target patterns with a laser spot. The mean positioning accuracy of the
spot is 0.5 mm. Finally, methods of image-guided robot control are introduced to automate laser
ablation. Experiments confirm a positive effect of proposed automation concepts on non-contact
laser surgery.Die Anwendung von Lasertechnologien in chirurgischen Interventionen hat sich aufgrund der atraumatischen Eigenschaften in der Klinik etabliert. Neben manueller Applikation von fasergefĂĽhrten
Lasern mit Gewebekontakt hat sich die kontaktfreie transorale Lasermikrochirurgie (TLM) von
Tumoren des Larynx in der HNO-Chirurgie durchgesetzt. Die TLM erfordert zur Tumorresektion
jedoch ein langjähriges chirurgisches Training, um die Funktion der angrenzenden Organe zu
sichern und damit die Lebensqualität der Patienten zu erhalten. Die Positionierung des mikroskopis chen Laserapplikators außerhalb des Patienten kann zudem die direkte Sicht auf das Zielgebiet
durch anatomische Variabilität erschweren und den Arbeitsraum einschränken. Weitere klinische
Herausforderungen betreffen die Positionierung des Laserfokus auf der Gewebeoberfläche, die
Bildgebung, die Planung und AusfĂĽhrung der Laserablation sowie intraoperative Bewegungen
des Zielgebietes. Die vorliegende Dissertation zielt darauf ab, die Limitierungen der TLM durch
robotische Ansätze und intraoperative Assistenz zu adressieren. Obwohl ein Trend zur minimal
invasiven Chirurgie besteht, sind bislang keine hochintegrierten Plattformen fĂĽr die endoskopische
Applikation fokussierter Laserstrahlung verfĂĽgbar. Ebenfalls sind keine Systeme bekannt, die
Szeneninformationen aus der endoskopischen Bildgebung in die Ablationsplanung und -ausfĂĽhrung
einbeziehen. Für eine situsnahe Fokussierung des Laserstrahls wird in dieser Arbeit zunächst
eine miniaturisierte Fokussieroptik zur Integration in endoskopische Systeme vorgestellt. Experimentelle Versuche charakterisieren die optischen Eigenschaften und das Ablationsverhalten. Zur
Manipulation der Fokussieroptik wird eine robotische Plattform realisiert. Diese basiert auf einem
längenveränderlichen Kontinuumsmanipulator. Letzterer ermöglicht in Kombination mit einer
mechatronischen Aktuierungseinheit Bewegungen des Endoskopkopfes in fĂĽnf Freiheitsgraden.
Die kinematische Modellierung und Regelung des Systems werden in ein modulares Framework
eingebunden und evaluiert. Die Manipulation fokussierter Laserstrahlung erfordert zudem eine
präzise Anpassung der Fokuslage auf das Gewebe. Dafür werden visuelle, haptische und visuell haptische Assistenzfunktionen eingeführt. Diese unterstützen den Anwender bei Teleoperation
zur Einstellung eines optimalen Arbeitsabstandes. In einer Anwenderstudie werden Vorteile der
visuell-haptischen Assistenz nachgewiesen. Die Systemperformanz und Gebrauchstauglichkeit
des robotischen Gesamtsystems werden in einer weiteren Anwenderstudie untersucht. Analog zu
einem klinischen Einsatz verfolgen die Probanden mit einem Laserspot vorgegebene Sollpfade. Die
mittlere Positioniergenauigkeit des Spots beträgt dabei 0,5 mm. Zur Automatisierung der Ablation
werden abschließend Methoden der bildgestützten Regelung vorgestellt. Experimente bestätigen
einen positiven Effekt der Automationskonzepte fĂĽr die kontaktfreie Laserchirurgie
Ultrasound-Augmented Laparoscopy
Laparoscopic surgery is perhaps the most common minimally invasive procedure for many diseases in the abdomen. Since the laparoscopic camera provides only the surface view of the internal organs, in many procedures, surgeons use laparoscopic ultrasound (LUS) to visualize deep-seated surgical targets. Conventionally, the 2D LUS image is visualized in a display spatially separate from that displays the laparoscopic video. Therefore, reasoning about the geometry of hidden targets requires mentally solving the spatial alignment, and resolving the modality differences, which is cognitively very challenging. Moreover, the mental representation of hidden targets in space acquired through such cognitive medication may be error prone, and cause incorrect actions to be performed.
To remedy this, advanced visualization strategies are required where the US information is visualized in the context of the laparoscopic video. To this end, efficient computational methods are required to accurately align the US image coordinate system with that centred in the camera, and to render the registered image information in the context of the camera such that surgeons perceive the geometry of hidden targets accurately. In this thesis, such a visualization pipeline is described. A novel method to register US images with a camera centric coordinate system is detailed with an experimental investigation into its accuracy bounds. An improved method to blend US information with the surface view is also presented with an experimental investigation into the accuracy of perception of the target locations in space
Towards disparity joint upsampling for robust stereoscopic endoscopic scene reconstruction in robotic prostatectomy
© 2016 SPIE. Three-dimensional (3-D) scene reconstruction from stereoscopic binocular laparoscopic videos is an effective way to expand the limited surgical field and augment the structure visualization of the organ being operated in minimally invasive surgery. However, currently available reconstruction approaches are limited by image noise, occlusions, textureless and blurred structures. In particular, an endoscope inside the body only has the limited light source resulting in illumination non-uniformities in the visualized field. These limitations unavoidably deteriorate the stereo image quality and hence lead to low-resolution and inaccurate disparity maps, resulting in blurred edge structures in 3-D scene reconstruction. This paper proposes an improved stereo correspondence framework that integrates cost-volume filtering with joint upsampling for robust disparity estimation. Joint bilateral upsampling, joint geodesic upsampling, and tree filtering upsampling were compared to enhance the disparity accuracy. The experimental results demonstrate that joint upsampling provides an effective way to boost the disparity estimation and hence to improve the surgical endoscopic scene 3-D reconstruction. Moreover, the bilateral upsampling generally outperforms the other two upsampling methods in disparity estimation