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

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

Automatic extraction of bronchus and centerline determination from CT images for three dimensional virtual bronchoscopy.

Law Tsui Ying.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 64-70).Abstracts in English and Chinese.Acknowledgments --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Structure of Bronchus --- p.3Chapter 1.2 --- Existing Systems --- p.4Chapter 1.2.1 --- Virtual Endoscope System (VES) --- p.4Chapter 1.2.2 --- Virtual Reality Surgical Simulator --- p.4Chapter 1.2.3 --- Automated Virtual Colonoscopy (AVC) --- p.5Chapter 1.2.4 --- QUICKSEE --- p.5Chapter 1.3 --- Organization of Thesis --- p.6Chapter 2 --- Three Dimensional Visualization in Medicine --- p.7Chapter 2.1 --- Acquisition --- p.8Chapter 2.1.1 --- Computed Tomography --- p.8Chapter 2.2 --- Resampling --- p.9Chapter 2.3 --- Segmentation and Classification --- p.9Chapter 2.3.1 --- Segmentation by Thresholding --- p.10Chapter 2.3.2 --- Segmentation by Texture Analysis --- p.10Chapter 2.3.3 --- Segmentation by Region Growing --- p.10Chapter 2.3.4 --- Segmentation by Edge Detection --- p.11Chapter 2.4 --- Rendering --- p.12Chapter 2.5 --- Display --- p.13Chapter 2.6 --- Hazards of Visualization --- p.13Chapter 2.6.1 --- Adding Visual Richness and Obscuring Important Detail --- p.14Chapter 2.6.2 --- Enhancing Details Incorrectly --- p.14Chapter 2.6.3 --- The Picture is not the Patient --- p.14Chapter 2.6.4 --- Pictures-'R'-Us --- p.14Chapter 3 --- Overview of Advanced Segmentation Methodologies --- p.15Chapter 3.1 --- Mathematical Morphology --- p.15Chapter 3.2 --- Recursive Region Search --- p.16Chapter 3.3 --- Active Region Models --- p.17Chapter 4 --- Overview of Centerline Methodologies --- p.18Chapter 4.1 --- Thinning Approach --- p.18Chapter 4.2 --- Volume Growing Approach --- p.21Chapter 4.3 --- Combination of Mathematical Morphology and Region Growing Schemes --- p.22Chapter 4.4 --- Simultaneous Borders Identification Approach --- p.23Chapter 4.5 --- Tracking Approach --- p.24Chapter 4.6 --- Distance Transform Approach --- p.25Chapter 5 --- Automated Extraction of Bronchus Area --- p.27Chapter 5.1 --- Basic Idea --- p.27Chapter 5.2 --- Outline of the Automated Extraction Algorithm --- p.28Chapter 5.2.1 --- Selection of a Start Point --- p.28Chapter 5.2.2 --- Three Dimensional Region Growing Method --- p.29Chapter 5.2.3 --- Optimization of the Threshold Value --- p.29Chapter 5.3 --- Retrieval of Start Point Algorithm Using Genetic Algorithm --- p.29Chapter 5.3.1 --- Introduction to Genetic Algorithm --- p.30Chapter 5.3.2 --- Problem Modeling --- p.31Chapter 5.3.3 --- Algorithm for Determining a Start Point --- p.33Chapter 5.3.4 --- Genetic Operators --- p.33Chapter 5.4 --- Three Dimensional Painting Algorithm --- p.34Chapter 5.4.1 --- Outline of the Three Dimensional Painting Algorithm --- p.34Chapter 5.5 --- Optimization of the Threshold Value --- p.36Chapter 6 --- Automatic Centerline Determination Algorithm --- p.38Chapter 6.1 --- Distance Transformations --- p.38Chapter 6.2 --- End Points Retrieval --- p.41Chapter 6.3 --- Graph Based Centerline Algorithm --- p.44Chapter 7 --- Experiments and Discussion --- p.48Chapter 7.1 --- Experiment of Automated Determination of Bronchus Algorithm --- p.48Chapter 7.2 --- Experiment of Automatic Centerline Determination Algorithm --- p.54Chapter 8 --- Conclusion --- p.62Bibliography --- p.6

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

Automatic colonic polyp detection using curvature analysis for standard and low dose CT data

Colon cancer is the second leading cause of cancer related deaths in the developed nations. Early detection and removal of colorectal polyps via screening is the most effective way to reduce colorectal cancer (CRC) mortality. Computed Tomography Colonography (CTC) or Virtual Colonoscopy (VC) is a rapidly evolving non-invasive technique and the medical community view this medical procedure as an alternative to the standard colonoscopy for the detection of colonic polyps. In CTC the first step for automatic polyp detection for 3D visualization of the colon structure and automatic polyp detection addresses the segmentation of the colon lumen. The segmentation of colon lumen is far from a trivial task as in practice many datasets are collapsed due to incorrect patient preparation or blockages caused by residual water/materials left in the colon. In this thesis a robust multi-stage technique for automatic segmentation of the colon is proposed tha t maximally uses the anatomical model of a generic colon. In this regard, the colon is reconstructed using volume by length analysis, orientation, length, end points, geometrical position in the volumetric data, and gradient of the centreline of each candidate air region detected in the CT data. The proposed method was validated using a total of 151 standard dose (lOOmAs) and 13 low-dose (13mAs-40mAs) datasets and the collapsed colon surface detection was always higher than 95% with an average of 1.58% extra colonic surface inclusion. The second major step of automated CTC attempts the identification of colorectal polyps. In this thesis a robust method for polyp detection based on surface curvature analysis has been developed and evaluated. The convexity of the segmented colon surface is sampled using the surface normal intersection, Hough transform, 3D histogram, Gaussian distribution, convexity constraint and 3D region growing. For each polyp candidate surface the morphological and statistical features are extracted and the candidate surface is classified as a polyp/fold structure using a Feature Normalized Nearest Neighbourhood classifier. The devised polyp detection scheme entails a low computational overhead (typically takes 3.60 minute per dataset) and shows 100% sensitivity for polyps larger than 10mm, 92% sensitivity for polyps in the range 5 to 10mm and 64.28% sensitivity for polyp smaller than 5mm. The developed technique returns in average 4.01 false positives per dataset. The patient exposure to ionising radiation is the major concern in using CTC as a mass screening technique for colonic polyp detection. A reduction of the radiation dose will increase the level of noise during the acquisition process and as a result the quality of the CT d a ta is degraded. To fully investigate the effect of the low-dose radiation on the performance of automated polyp detection, a phantom has been developed and scanned using different radiation doses. The phantom polyps have realistic shapes (sessile, pedunculated, and flat) and sizes (3 to 20mm) and were designed to closely approximate the real polyps encountered in clinical CT data. Automatic polyp detection shows 100% sensitivity for polyps larger than 10mm and shows 95% sensitivity for polyps in the range 5 to 10mm. The developed method was applied to CT data acquired at radiation doses between 13 to 40mAs and the experimental results indicate th a t robust polyp detection can be obtained even at radiation doses as low as 13mAs

Visualisation of curved tubular structures in medical databases: An application to virtual colonoscopy

Medical conditions affecting the colon are problematic to diagnose due to the difficulty in examining this particular internal organ. To date, the most widely used approach is to perform a colonoscopy; a procedure in which a small camera is inserted into the colon to examine its surface. This procedure is unpleasant and potentially dangerous for the patient, and is expensive and time consuming for the hospital. As a result, patients at risk of developing the conditions are not always screened as often as would be desirable. Over the last few years a new approach known as virtual colonoscopy has been gaining popularity. The method uses information from a CT scan to reconstruct a 3D model of the colon which can then be examined without the patient needing to undergo a colonoscopy. This approach is now commonly used when screening for polyps (an indication of colon cancer) but can not be so easily used on conditions such as Inflammatory Bowel Disease (IBD) where information beyond the shape of the surface is required. This thesis forms part of a larger project which aims to diagnose conditions such as IBD by using image processing algorithms on CT data and presenting the results to the user in an easy to interpret way. Specifically we are concerned with this visualisation stage of the system and so have developed a new visualisation approach which we call Volumetric CPR. This can be used to supplement the more traditional virtual flythrough visualisation and is applicable to IBD detection as well as screening for polyps. Our technique builds on the concept of Curved Planar Reformation (CPR), which has proved to be a practical and widely used tool for the visualisation of curved tubular structures within the human body. It has been useful in medical procedures involving the examination of blood vessels and the spine. However, it is more difficult to use it for structures such as the colon because abnormalities are smaller relative to the size of the structure and may not have such distinct density and shape characteristics. Our new approach improves on this situation by using volume rendering for hollow regions of the structure and standard CPR, for the surrounding tissue. This effectively combines grey scale contextual information with detailed colour information from the area of interest. The approach is successfully used with each of the standard CPR types and the resulting images are promising as an alternative for virtual colonoscopy. We also demonstrate how systems can effectively utilize this new visualisation in order to convey maximum information to the user. We show how overlays can be used to present surface coverage data and how sophisticated lighting models can improve the users understanding of the 3D structure. We also present details of how to integrate our visualisation into existing systems and work flows

Aquatics reconstruction software: the design of a diagnostic tool based on computer vision algorithms

Computer vision methods can be applied to a variety of medical and surgical applications, and many techniques and algorithms are available that can be used to recover 3D shapes and information from images range and volume data. Complex practical applications, however, are rarely approachable with a single technique, and require detailed analysis on how they can be subdivided in subtasks that are computationally treatable and that, at the same time, allow for the appropriate level of user-interaction. In this paper we show an example of a complex application where, following criteria of efficiency, reliability and user friendliness, several computer vision techniques have been selected and customized to build a system able to support diagnosis and endovascular treatment of Abdominal Aortic Aneurysms. The system reconstructs the geometrical representation of four different structures related to the aorta (vessel lumen, thrombus, calcifications and skeleton) from CT angiography data. In this way it supports the three dimensional measurements required for a careful geometrical evaluation of the vessel, that is fundamental to decide if the treatment is necessary and to perform, in this case, its planning. The system has been realized within the European trial AQUATICS (IST-1999-20226 EUTIST-M WP 12), and it has been widely tested on clinical data