Crowdsourcing for Identification of Polyp-Free Segments in Virtual Colonoscopy Videos

Virtual colonoscopy (VC) allows a physician to virtually navigate within a reconstructed 3D colon model searching for colorectal polyps. Though VC is widely recognized as a highly sensitive and specific test for identifying polyps, one limitation is the reading time, which can take over 30 minutes per patient. Large amounts of the colon are often devoid of polyps, and a way of identifying these polyp-free segments could be of valuable use in reducing the required reading time for the interrogating radiologist. To this end, we have tested the ability of the collective crowd intelligence of non-expert workers to identify polyp candidates and polyp-free regions. We presented twenty short videos flying through a segment of a virtual colon to each worker, and the crowd was asked to determine whether or not a possible polyp was observed within that video segment. We evaluated our framework on Amazon Mechanical Turk and found that the crowd was able to achieve a sensitivity of 80.0% and specificity of 86.5% in identifying video segments which contained a clinically proven polyp. Since each polyp appeared in multiple consecutive segments, all polyps were in fact identified. Using the crowd results as a first pass, 80% of the video segments could in theory be skipped by the radiologist, equating to a significant time savings and enabling more VC examinations to be performed

The use of 3D surface fitting for robust polyp detection and classification in CT colonography

In this paper we describe the development of a computationally efficient computer-aided detection (CAD) algorithm based on the evaluation of the surface morphology that is employed for the detection of colonic polyps in computed tomography (CT) colonography. Initial polyp candidate voxels were detected using the surface normal intersection values. These candidate voxels were clustered using the normal direction, convexity test, region growing and Gaussian distribution. The local colonic surface was classified as polyp or fold using a feature normalized nearest neighborhood classifier. The main merit of this paper is the methodology applied to select the robust features derived from the colon surface that have a high discriminative power for polyp/fold classification. The devised polyp detection scheme entails a low computational overhead (typically takes 2.20 min per dataset) and shows 100% sensitivity for phantom polyps greater than 5 mm. It also shows 100% sensitivity for real polyps larger than 10 mm and 91.67% sensitivity for polyps between 5 to 10 mm with an average of 4.5 false positives per dataset. The experimental data indicates that the proposed CAD polyp detection scheme outperforms other techniques that identify the polyps using features that sample the colon surface curvature especially when applied to low-dose datasets

Coral Disease and Health Workshop: Coral Histopathology II

The health and continued existence of coral reef ecosystems are threatened by an increasing array of environmental and anthropogenic impacts. Coral disease is one of the prominent causes of increased mortality among reefs globally, particularly in the Caribbean. Although over 40 different coral diseases and syndromes have been reported worldwide, only a few etiological agents have been confirmed; most pathogens remain unknown and the dynamics of disease transmission, pathogenicity and mortality are not understood. Causal relationships have been documented for only a few of the coral diseases, while new syndromes continue to emerge. Extensive field observations by coral biologists have provided substantial documentation of a plethora of new pathologies, but our understanding, however, has been limited to descriptions of gross lesions with names reflecting these observations (e.g., black band, white band, dark spot). To determine etiology, we must equip coral diseases scientists with basic biomedical knowledge and specialized training in areas such as histology, cell biology and pathology. Only through combining descriptive science with mechanistic science and employing the synthesis epizootiology provides will we be able to gain insight into causation and become equipped to handle the pending crisis. One of the critical challenges faced by coral disease researchers is to establish a framework to systematically study coral pathologies drawing from the field of diagnostic medicine and pathology and using generally accepted nomenclature. This process began in April 2004, with a workshop titled Coral Disease and Health Workshop: Developing Diagnostic Criteria co-convened by the Coral Disease and Health Consortium (CDHC), a working group organized under the auspices of the U.S. Coral Reef Task Force, and the International Registry for Coral Pathology (IRCP). The workshop was hosted by the U.S. Geological Survey, National Wildlife Health Center (NWHC) in Madison, Wisconsin and was focused on gross morphology and disease signs observed in the field. A resounding recommendation from the histopathologists participating in the workshop was the urgent need to develop diagnostic criteria that are suitable to move from gross observations to morphological diagnoses based on evaluation of microscopic anatomy. (PDF contains 92 pages

Superpixel Based Segmentation and Classification of Polyps in Wireless Capsule Endoscopy

Wireless Capsule Endoscopy (WCE) is a relatively new technology to record the entire GI trace, in vivo. The large amounts of frames captured during an examination cause difficulties for physicians to review all these frames. The need for reducing the reviewing time using some intelligent methods has been a challenge. Polyps are considered as growing tissues on the surface of intestinal tract not inside of an organ. Most polyps are not cancerous, but if one becomes larger than a centimeter, it can turn into cancer by great chance. The WCE frames provide the early stage possibility for detection of polyps. Here, the application of simple linear iterative clustering (SLIC) superpixel for segmentation of polyps in WCE frames is evaluated. Different SLIC superpixel numbers are examined to find the highest sensitivity for detection of polyps. The SLIC superpixel segmentation is promising to improve the results of previous studies. Finally, the superpixels were classified using a support vector machine (SVM) by extracting some texture and color features. The classification results showed a sensitivity of 91%.Comment: This paper has been published in SPMB 201

Under the sea: Pulsing corals in ambient flow

While many organisms filter feed and exchange heat or nutrients in flow, few benthic organisms also actively pulse to enhance feeding and exchange. One example is the pulsing soft coral (Heteroxenia fuscescens). Pulsing corals live in colonies, where each polyp actively pulses through contraction and relaxation of their tentacles. The pulses are typically out of phase and without a clear pattern. These corals live in lagoons and bays found in the Red Sea and Indian Ocean where they at times experience strong ambient flows. In this paper, $3D$ fluid-structure interaction simulations are used to quantify the effects of ambient flow on the exchange currents produced by the active contraction of pulsing corals. We find a complex interaction between the flows produced by the coral and the background flow. The dynamics can either enhance or reduce the upward jet generated in a quiescent medium. The pulsing behavior also slows the average horizontal flow near the polyp when there is a strong background flow. The dynamics of these flows have implications for particle capture and nutrient exchange.Comment: 7 pages, 9 Figure

Endocuff Vision Reduces Inspection Time Without Decreasing Lesion Detection in a Randomized Colonoscopy Trial

Background & Aims Mucosal exposure devices improve detection of lesions during colonoscopy and have reduced examination times in uncontrolled studies. We performed a randomized trial of Endocuff Vision vs standard colonoscopy to compare differences in withdrawal time (the primary end point). We proposed that Endocuff Vision would allow complete mucosal inspection in a shorter time without impairing lesion detection. Methods Adults older than 40 years undergoing screening or surveillance colonoscopies were randomly assigned to the Endocuff group (n=101, 43.6% women) or the standard colonoscopy group (n=99; 57.6% women). One of 2 experienced endoscopists performed the colonoscopies, aiming for a thorough evaluation of the proximal sides of all haustral folds, flexures, and valves in the shortest time possible. Inspection time was measured with a stopwatch and calculated by subtracting washing, suctioning, polypectomy and biopsy times from total withdrawal time. Results There were significantly fewer women in the Endocuff arm (P = .0475) but there were no other demographic differences between groups. Mean insertion time with Endocuff was 4.0 min vs 4.4 min for standard colonoscopy (P = .14). Mean inspection time with Endocuff was 6.5 min vs 8.4 min for standard colonoscopy (P < .0001). Numbers of adenomas detected per colonoscopy (1.43 vs 1.07; P = .07), adenoma detection rate (61.4% vs 52%; P = .21), number of sessile serrated polyps per colonoscopy (0.27 vs 0.21; P = .12), and sessile serrated polyp detection rate (19.8% vs 11.1%; P = .09) were all higher with Endocuff Vision. Results did not differ significantly when we controlled for age, sex, or race. Conclusion In a randomized trial, we found inclusion of Endocuff in screening or surveillance colonoscopies to decrease examination time without reducing lesion detection

Automated polyp detection in colon capsule endoscopy

Colorectal polyps are important precursors to colon cancer, a major health problem. Colon capsule endoscopy (CCE) is a safe and minimally invasive examination procedure, in which the images of the intestine are obtained via digital cameras on board of a small capsule ingested by a patient. The video sequence is then analyzed for the presence of polyps. We propose an algorithm that relieves the labor of a human operator analyzing the frames in the video sequence. The algorithm acts as a binary classifier, which labels the frame as either containing polyps or not, based on the geometrical analysis and the texture content of the frame. The geometrical analysis is based on a segmentation of an image with the help of a mid-pass filter. The features extracted by the segmentation procedure are classified according to an assumption that the polyps are characterized as protrusions that are mostly round in shape. Thus, we use a best fit ball radius as a decision parameter of a binary classifier. We present a statistical study of the performance of our approach on a data set containing over 18,900 frames from the endoscopic video sequences of five adult patients. The algorithm demonstrates a solid performance, achieving 47% sensitivity per frame and over 81% sensitivity per polyp at a specificity level of 90%. On average, with a video sequence length of 3747 frames, only 367 false positive frames need to be inspected by a human operator.Comment: 16 pages, 9 figures, 4 table

An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN

Polyp has long been considered as one of the major etiologies to colorectal cancer which is a fatal disease around the world, thus early detection and recognition of polyps plays a crucial role in clinical routines. Accurate diagnoses of polyps through endoscopes operated by physicians becomes a challenging task not only due to the varying expertise of physicians, but also the inherent nature of endoscopic inspections. To facilitate this process, computer-aid techniques that emphasize fully-conventional image processing and novel machine learning enhanced approaches have been dedicatedly designed for polyp detection in endoscopic videos or images. Among all proposed algorithms, deep learning based methods take the lead in terms of multiple metrics in evolutions for algorithmic performance. In this work, a highly effective model, namely the faster region-based convolutional neural network (Faster R-CNN) is implemented for polyp detection. In comparison with the reported results of the state-of-the-art approaches on polyps detection, extensive experiments demonstrate that the Faster R-CNN achieves very competing results, and it is an efficient approach for clinical practice.Comment: 6 pages, 10 figures,2018 International Conference on Pattern Recognitio