Advanced gastrointestinal endoscopic imaging for inflammatory bowel diseases

Gastrointestinal luminal endoscopy is of paramount importance for diagnosis, monitoring and dysplasia surveillance in patients with both, Crohn's disease and ulcerative colitis. Moreover, with the recent recognition that mucosal healing is directly linked to the clinical outcome of patients with inflammatory bowel disorders, a growing demand exists for the precise, timely and detailed endoscopic assessment of superficial mucosal layer. Further, the novel field of molecular imaging has tremendously expanded the clinical utility and applications of modern endoscopy, now encompassing not only diagnosis, surveillance, and treatment but also the prediction of individual therapeutic responses. Within this review, we describe how novel endoscopic approaches and advanced endoscopic imaging methods such as high definition and high magnification endoscopy, dye-based and dye-less chromoendoscopy, confocal laser endomicroscopy, endocytoscopy and molecular imaging now allow for the precise and ultrastructural assessment of mucosal inflammation and describe the potential of these techniques for dysplasia detection

Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos

There are several types of disorders that affect our colon’s ability to function properly such as colorectal cancer, ulcerative colitis, diverticulitis, irritable bowel syndrome and colonic polyps. Automatic detection of these diseases would inform the endoscopist of possible sub-optimal inspection during the colonoscopy procedure as well as save time during post-procedure evaluation. But existing systems only detects few of those disorders like colonic polyps. In this dissertation, we address the automatic detection of another important disorder called ulcerative colitis. We propose a novel texture feature extraction technique to detect the severity of ulcerative colitis in block, image, and video levels. We also enhance the current informative frame filtering methods by detecting water and bubble frames using our proposed technique. Our feature extraction algorithm based on accumulation of pixel value difference provides better accuracy at faster speed than the existing methods making it highly suitable for real-time systems. We also propose a hybrid approach in which our feature method is combined with existing feature method(s) to provide even better accuracy. We extend the block and image level detection method to video level severity score calculation and shot segmentation. Also, the proposed novel feature extraction method can detect water and bubble frames in colonoscopy videos with very high accuracy in significantly less processing time even when clustering is used to reduce the training size by 10 times

From the surface to the single cell: Novel endoscopic approaches in inflammatory bowel disease

Inflammatory bowel diseases (IBD) comprise the two major entities Crohn's disease and ulcerative colitis and endoscopic imaging of the gastrointestinal tract has always been an integral and central part in the management of IBD patients. Within the recent years, mucosal healing emerged as a key treatment goal in IBD that substantially decides about the clinical outcome of IBD patients, thereby demanding for a precise, timely and detailed endoscopic assessment of the mucosal inflammation associated with IBD. Further, molecular imaging has tremendously expanded the clinical utility and applications of modern endoscopy, now encompassing not only diagnosis, surveillance, and treatment but also the prediction of individual therapy response. Within this review we describe novel endoscopic approaches and advanced endoscopic imaging methods for the diagnosis, treatment and surveillance of IBD patients. We begin by providing an overview over novel and advanced imaging techniques such as magnification endoscopy and dye-based and dye-less chromoendoscopy, endomicroscopy and endocytoscopy. We then describe how these techniques can be utilized for the precise and ultrastructural assessment of mucosal inflammation and dysplasia development associated with IBD and outline how they have enabled the endoscopist to gain insight onto the cellular level in real-time. Finally, we provide an outlook on how molecular imaging has rapidly evolved in the recent past and can be used to make individual predictions about the therapeutic response towards biological treatment

New endoscopic tools in inflammatory bowel disease

Endoscopic remission is now considered the ultimate long‐term goal for treating inflammatory bowel disease (IBD). Recent advances in endoscopic techniques have progressively added new tools to the armamentarium of endoscopists for a deeper assessment and characterisation of the intestinal mucosa. Virtual Electronic chromoendoscopy is widely available in the endoscopic units, leading to a more accurate evaluation of the vascular and mucosal architecture of the colon, reducing the gap with histology, which is considered a favourable long‐term measure. In addition, advanced, sophisticated techniques such as endocytoscope and confocal laser endomicroscopy provide insights into individualised and personalised IBD therapy. Finally, high expectations are placed on the advent of Artificial Intelligence (AI) with promising applications that have the potential to revolutionise IBD diagnosis and management. Here, we discuss state‐of‐the‐art of endoscopic techniques and their applicability to accurate assess endoscopic and histological remission, predict response to therapy and detect, characterise and guide treatment of colonic dysplastic lesions. We are seeing the dawn of a new era wherein the applications of these new endoscopic tools, hand in hand with AI, offer the most incredible opportunity to deliver precision medicine to patients with IBD

Confocal Laser Endomicroscopy in the Study of Colonic Mucosa in IBD Patients: A Review

Confocal laser endomicroscopy (CLE) is one of several novel methods that provide real-time, high-resolution imaging at a micronscale via endoscopes. CLE and related technologies are often termed “virtual biopsy” as they simulate the images seen in traditional histology. Recently, the use of CLE was reported in the study of colonic mucosa in patients with inflammatory bowel diseases and in particular in patients affected by ulcerative colitis. CLE has the potential to have an important role in management of IBD patients as it can be used to assess the grading of colitis and in detection of microscopic colitis in endoscopically silent segments. Moreover, CLE can be used in surveillance programs especially in high-risk patients. This report aims to evaluate the current data on the application of confocal endomicroscopy in clinical gastroenterology and particularly in the study of colonic mucosa in UC patients

Detection of colonic dysplasia in patients with ulcerative colitis using a targeted fluorescent peptide and confocal laser endomicroscopy: A pilot study

Targeted molecular probes have been used to detect sporadic colonic dysplasia during confocal laser endomicroscopy (CLE) with promising results. This is a feasibility pilot study aiming to assess the potential role of CLE combined with a fluorescent-labeled peptide to stain and detect dysplasia associated with Ulcerative Colitis

Kvasir-Capsule, a video capsule endoscopy dataset

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology