Computer-Assisted Segmentation of Videocapsule Images Using Alpha-Divergence-Based Active Contour in the Framework of Intestinal Pathologies Detection

Visualization of the entire length of the gastrointestinal tract through natural orifices is a challenge for endoscopists. Videoendoscopy is currently the “gold standard” technique for diagnosis of different pathologies of the intestinal tract. Wireless Capsule Endoscopy (WCE) has been developed in the 1990's as an alternative to videoendoscopy to allow direct examination of the gastrointestinal tract without any need for sedation. Nevertheless, the systematic post-examination by the specialist of the 50,000 (for the small bowel) to 150,000 images (for the colon) of a complete acquisition using WCE remains time-consuming and challenging due to the poor quality of WCE images. In this article, a semiautomatic segmentation for analysis of WCE images is proposed. Based on active contour segmentation, the proposed method introduces alpha-divergences, a flexible statistical similarity measure that gives a real flexibility to different types of gastrointestinal pathologies. Results of segmentation using the proposed approach are shown on different types of real-case examinations, from (multi-) polyp(s) segmentation, to radiation enteritis delineation

Selective diffusion for oriented pattern extraction: Application to tagged cardiac MRI enhancement

Anisotropic regularization PDE’s (Partial Differential Equation) raised a strong interest in the field of image processing. The benefit of PDE-based regularization methods lies in the ability to smooth data in a nonlinear way, allowing the preservation of important image features (contours, corners or other discontinuities). In this article, a selective diffusion approach based on the framework of Extreme Physical Information theory is presented. It is shown that this particular framework leads to a particular regularization PDE which makes the integration of prior knowledge possible within the diffusion scheme. As a proof of feasibility, results of oriented pattern extractions are first presented on ad hoc images and second on a particular medical application: Tagged cardiac MRI (Magnetic Resonance Imaging) enhancement

Statistical region based active contour using a fractional entropy descriptor: Application to nuclei cell segmentation in confocal microscopy images

We propose an unsupervised statistical region based active contour approach integrating an original fractional entropy measure for image segmentation with a particular application to single channel actin tagged fluorescence confocal microscopy image segmentation. Following description of statistical based active contour segmentation and the mathematical definition of the proposed fractional entropy descriptor, we demonstrate comparative segmentation results between the proposed approach and standard Shannon’s entropy on synthetic and natural images. We also show that the proposed unsupervised statistical based approach, integrating the fractional entropy measure, leads to very satisfactory segmentation of the cell nuclei from which shape characterization can be calculated

Noise-enhanced nonlinear PDE for edge restoration in scalar images

The report proposed an interpretation for the mechanism of noise-enhanced image restoration with nonlinear PDE (Partial Differential Equation) recently demonstrated in literature. A link is established between the action of noise in a nonlinear Perona-Malik anisotropic diffusion and stochastic resonance in memoryless nonlinear systems for 1-D signals

Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation

This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data

Establishing key research questions for the implementation of artificial intelligence in colonoscopy - a modified Delphi method

Background and Aims Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. Methods An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers from 9 countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. Results The top 10 ranked questions were categorised into 5 themes. Theme 1: Clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterisation, determining the optimal end-points for evaluation of AI and demonstrating impact on interval cancer rates. Theme 2: Technological Developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false positive rates and minimising latency. Theme 3: Clinical adoption/Integration (1 question) concerning effective combination of detection and characterisation into one workflow. Theme 4: Data access/annotation (1 question) concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: Regulatory Approval (1 question) related to making regulatory approval processes more efficient. Conclusions This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy

Active contour segmentation based on approximate entropy: Application to cell membrane segmentation in confocal microscopy

Segmentation of cellular structures is of primary interest in cell imaging for cell shape reconstruction and to provide crucial information about possible cell morphology changes during radiotherapy for instance. From the particular perspective of predictive oncology, this paper reports on a novel method for membrane segmentation from single channel actin tagged fluorescence confocal microscopy images, which remains a challenging task. Proposed method is based on the use of the Approximate Entropy formerly introduced by Pincus embedded within a Geodesic Active Contour approach. Approximate Entropy can be seen as an estimator of the regularity of a particular sequence of values and, consequently, can be used as an edge detector. In this prospective study, a preliminary study on Approximate Entropy as an edge detector function is first proposed with a particular focus on the robustness to noise, and some promising membrane segmentation results obtained on confocal microscopy images are also shown. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved

Artificial intelligence in small bowel capsule endoscopy - current status, challenges and future promise

Neural network-based solutions are under development to alleviate physicians from the tedious task of small-bowel capsule endoscopy reviewing. Computer-assisted detection is a critical step, aiming to reduce reading times while maintaining accuracy. Weakly supervised solutions have shown promising results; however, video-level evaluations are scarce, and no prospective studies have been conducted yet. Automated characterization (in terms of diagnosis and pertinence) by supervised machine learning solutions is the next step. It relies on large, thoroughly labeled databases, for which preliminary “ground truth” definitions by experts are of tremendous importance. Other developments are under ways, to assist physicians in localizing anatomical landmarks and findings in the small bowel, in measuring lesions, and in rating bowel cleanliness. It is still questioned whether artificial intelligence will enter the market with proprietary, built-in or plug-in software, or with a universal cloud-based service, and how it will be accepted by physicians and patients. © 2020 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Lt