Registration of the endoluminal surfaces of the colon derived from prone and supine CT colonography

Purpose: Computed tomographic (CT) colonography is a relatively new technique for detecting bowel cancer or potentially precancerous polyps. CT scanning is combined with three-dimensional (3D) image reconstruction to produce a virtual endoluminal representation similar to optical colonoscopy. Because retained fluid and stool can mimic pathology, CT data are acquired with the bowel cleansed and insufflated with gas and patient in both prone and supine positions. Radiologists then match visually endoluminal locations between the two acquisitions in order to determine whether apparent pathology is real or not. This process is hindered by the fact that the colon, essentially a long tube, can undergo considerable deformation between acquisitions. The authors present a novel approach to automatically establish spatial correspondence between prone and supine endoluminal colonic surfaces after surface parameterization, even in the case of local colon collapse.Methods: The complexity of the registration task was reduced from a 3D to a 2D problem by mapping the surfaces extracted from prone and supine CT colonography onto a cylindrical parameterization. A nonrigid cylindrical registration was then performed to align the full colonic surfaces. The curvature information from the original 3D surfaces was used to determine correspondence. The method can also be applied to cases with regions of local colonic collapse by ignoring the collapsed regions during the registration.Results: Using a development set, suitable parameters were found to constrain the cylindrical registration method. Then, the same registration parameters were applied to a different set of 13 validation cases, consisting of 8 fully distended cases and 5 cases exhibiting multiple colonic collapses. All polyps present were well aligned, with a mean (+/- std. dev.) registration error of 5.7 (+/- 3.4) mm. An additional set of 1175 reference points on haustral folds spread over the full endoluminal colon surfaces resulted in an error of 7.7 (+/- 7.4) mm. Here, 82% of folds were aligned correctly after registration with a further 15% misregistered by just onefold.Conclusions: The proposed method reduces the 3D registration task to a cylindrical registration representing the endoluminal surface of the colon. Our algorithm uses surface curvature information as a similarity measure to drive registration to compensate for the large colorectal deformations that occur between prone and supine data acquisitions. The method has the potential to both enhance polyp detection and decrease the radiologist's interpretation time. (C) 2011 American Association of Physicists in Medicine. [DOI: 10.1118/1.3577603

Virtual colon unfolding for polyp detection

Master'sMASTER OF ENGINEERIN

Facilitating Colorectal Cancer Diagnosis with Computed Tomographic Colonography

Computed tomographic colonography (CTC) is a diagnostic technique involving helical volume acquisition of the cleansed, distended colorectum to detect colorectal cancer or potentially premalignant polyps. This Thesis summarises the evidence base, identifies areas in need of further research, quantifies sources of bias and presents novel techniques to facilitate colorectal cancer diagnosis using CTC. CTC literature is reviewed to justify the rationale for current implementation and to identify fruitful areas for research. This confirms excellent diagnostic performance can be attained providing CTC is interpreted by trained, experienced observers employing state-of-the-art implementation. The technique is superior to barium enema and consequently, it has been embraced by radiologists, clinicians and health policy-makers. Factors influencing generalisability of CTC research are investigated, firstly with a survey of European educational workshop participants which revealed limited CTC experience and training, followed by a systematic review exploring bias in research studies of diagnostic test accuracy which established that studies focussing on these aspects were lacking. Experiments to address these sources of bias are presented, using novel methodology: Conjoint analysis is used to ascertain patients‘ and clinicians’ attitudes to false-positive screening diagnoses, showing that both groups overwhelmingly value sensitivity over specificity. The results inform a weighted statistical analysis for CAD which is applied to the results of two previous studies showing the incremental benefit is significantly higher for novices than experienced readers. We have employed eye-tracking technology to establish the visual search patterns of observers reading CTC, demonstrated feasibility and developed metrics for analysis. We also describe development and validation of computer software to register prone and supine endoluminal surface locations demonstrating accurate matching of corresponding points when applied to a phantom and a generalisable, publically available, CTC database. Finally, areas in need of future development are suggested

Registration of prone and supine CT colonography images and its clinical application

Computed tomographic (CT) colonography is a technique for detecting bowel cancer and potentially precancerous polyps. CT imaging is performed on the cleansed and insufflated bowel in order to produce a virtual endoluminal representation similar to optical colonoscopy. Because fluids and stool can mimic pathology, images are acquired with the patient in both prone and supine positions. Radiologists then match endoluminal locations visually between the two acquisitions in order to determine whether pathology is real or not. This process is hindered by the fact that the colon can undergo considerable deformation between acquisitions. Robust and accurate automated registration between prone and supine data acquisitions is therefore pivotal for medical interpretation, but a challenging problem. The method proposed in this thesis reduces the complexity of the registration task of aligning the prone and supine CT colonography acquisitions. This is done by utilising cylindrical representations of the colonic surface which reflect the colon's specific anatomy. Automated alignment in the cylindrical domain is achieved by non-rigid image registration using surface curvatures, applicable even when cases exhibit local luminal collapses. It is furthermore shown that landmark matches for initialisation improve the registration's accuracy and robustness. Additional performance improvements are achieved by symmetric and inverse-consistent registration and iteratively deforming the surface in order to compensate for differences in distension and bowel preparation. Manually identified reference points in human data and fiducial markers in a porcine phantom are used to validate the registration accuracy. The potential clinical impact of the method has been evaluated using data that reflects clinical practise. Furthermore, correspondence between follow-up CT colonography acquisitions is established in order to facilitate the clinical need to investigate polyp growth over time. Accurate registration has the potential to both improve the diagnostic process and decrease the radiologist's interpretation time. Furthermore, its result could be integrated into algorithms for improved computer-aided detection of colonic polyps

Appearance Modelling and Reconstruction for Navigation in Minimally Invasive Surgery

Minimally invasive surgery is playing an increasingly important role for patient care. Whilst its direct patient benefit in terms of reduced trauma, improved recovery and shortened hospitalisation has been well established, there is a sustained need for improved training of the existing procedures and the development of new smart instruments to tackle the issue of visualisation, ergonomic control, haptic and tactile feedback. For endoscopic intervention, the small field of view in the presence of a complex anatomy can easily introduce disorientation to the operator as the tortuous access pathway is not always easy to predict and control with standard endoscopes. Effective training through simulation devices, based on either virtual reality or mixed-reality simulators, can help to improve the spatial awareness, consistency and safety of these procedures. This thesis examines the use of endoscopic videos for both simulation and navigation purposes. More specifically, it addresses the challenging problem of how to build high-fidelity subject-specific simulation environments for improved training and skills assessment. Issues related to mesh parameterisation and texture blending are investigated. With the maturity of computer vision in terms of both 3D shape reconstruction and localisation and mapping, vision-based techniques have enjoyed significant interest in recent years for surgical navigation. The thesis also tackles the problem of how to use vision-based techniques for providing a detailed 3D map and dynamically expanded field of view to improve spatial awareness and avoid operator disorientation. The key advantage of this approach is that it does not require additional hardware, and thus introduces minimal interference to the existing surgical workflow. The derived 3D map can be effectively integrated with pre-operative data, allowing both global and local 3D navigation by taking into account tissue structural and appearance changes. Both simulation and laboratory-based experiments are conducted throughout this research to assess the practical value of the method proposed

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

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

Enhancement of spatial awareness in natural orifice transluminal endoscopic surgery

Natural orifice transluminal endoscopic surgery (NOTES) represents a challenging concept to pursue in minimally invasive procedures, with a promise of becoming even less invasive, but with the additional issues of being team dependent and more technology intensive. The safety of patients undergoing any surgical procedure is of principal importance. When a surgeon performs laparoscopic procedures, he only has a two dimensional field of view and as a result, his spatial awareness is diminished. A surgeon operating under conditions of reduced spatial awareness poses an increased risk to a patient. Spatial awareness is deemed a necessary skill for the safe deployment of NOTES procedures. Understanding the surgeon’s situational and spatial awareness during NOTES investigation is therefore of paramount importance for the safe performance of this type of procedures. Enhancing scene visualisation, for instance by means of additional viewpoints or electromagnetic tracking, seems a feasible strategy for augmenting spatial awareness in NOTES. This study aims to propose novel approaches involving electromagnetic tracking and additional off-axis visualisation in an attempt to assess, as well as enhance spatial awareness of the operating field in NOTES. The original contributions of this thesis include: • Validation of an outlining approach to characterise spatial awareness in minimally invasive surgery particularly in NOTES • Qualitative and quantitative assessment of spatial awareness n NOTES • Identification of certain navigation behavioural patterns in NOTES • Design and evaluation of spatial awareness enhancement tool for NOTES The value of the research presented in this thesis, as well as the potential for further development is also discussed in the context of spatial awareness in MIS in general