Generic Feature Learning for Wireless Capsule Endoscopy Analysis

The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase)

Evaluation of intestinal motility with Magnetic Resonance Enterography and computer post-processing

Small bowel motility is an essential, physiological process central to the processing of ingested food. The small bowel is however anatomically and functionally complex, varying greatly between individuals and located deep within the abdomen making it extremely difficult to access with instrumentation. As a consequence, and in spite of its known or suspected role in a range of diseases, there remain little in the way of objective tests to evaluate or even observe this process in vivo. This thesis details the validation and application of a novel computer post-processing technique that allows the quantification of Magnetic Resonance Enterography derived time-series image data. A background to small bowel physiology and existing techniques is first provided along with an introduction to the registration algorithm used throughout this thesis to quantify small bowel motility. The technique is then applied retrospectively to two Crohn’s disease patient cohorts to explore how this inflammatory bowel disease influences contractility. A prospective evaluation of segmental motility analysis is then presented drawing attention to large within subject variation, in a cohort of healthy volunteers, as a limitation for this technique. As an alternative, a global motility analysis approach is described and validated. Although global measures of motility appeared robust, factors influencing clinical application are further addressed by expanding the technique to allow motility analysis in free-breathing data. In the final piece of research presented, the application of the global technique to a cohort of Chronic Intestinal Pseudo-Obstruction patients is detailed. The thesis is concluded with a reflection of the results and a chapter dedicated to the commercial exploitation of the research to address the ongoing need for a robust test to quantise intestinal motility

Small bowel motility quantitation using MRI and its relationship to gastrointestinal symptoms

The small bowel is difficult to analyse due to its deep anatomical location and the large variation seen in individuals, in regard to both anatomy and function including motility. Dynamic MRI allows small bowel motility to be captured and visually assessed by radiologists, but there is often large inter-observer variation and a lack of complicated motility patterns being investigated. This thesis aims to explore the link between abnormal motility and gastrointestinal (GI) symptoms in Crohn’s disease (CD) and irritable bowel syndrome (IBS) using MRI. Firstly, a scan duration of 15 seconds and a temporal resolution of 1 image per second were shown to be sufficient for robust small bowel MRI motility measurements. Next, a validation study confirmed an association between aberrant motility and CD patient symptoms, particularly diarrhoeal stools (rho = -0.29). The strongest association was in patients with higher symptom severity (rho = -0.633). Building on this work, more complex motility metrics were developed and compared to subjective radiological scoring. Spatial and temporal variation were found to be associated with CD patient symptoms and were also particularly difficult to visually assess. The motility metrics were applied in clinical IBS data to explore differences in IBS subgroups. Significantly reduced temporal variation of motility (P < 0.001) and area of motile bowel (P < 0.001) was found in IBS-C (constipation-predominant) compared to IBS-M (mixed constipation and diarrhoea). Finally, texture analysis (TA) terminal ileum (TI) to colon ratios were found to be higher for TA contrast (P = 0.005) and lower for TA energy (P = 0.03) in IBS-C compared to healthy controls (HCs). Ascending colon diameter was shown to be significantly larger in IBS-C than HCs (P = 0.005)