MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation.

Photoplethysmography and its application in clinical physiological measurement

Follow up what we will offer in this article about philosophical transactions of the royal society of london series b biological sciences no 600 vol 233 studies of the post glacial history of british vegetation x correlation between climate forest composition prehistoric agriculture and peat st. You know really that this book is coming as the best seller book today. So, when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book. It means that you have to get this book. For you who are starting to learn about something new and feel curious about this book, it's easy then. Just get this book and feel how this book will give you more exciting lessons.

Philosophical Transactions of the Royal Society of London. for the Year Mdcccxxvii. Part I. Volume Cxvii:355–388, 1827

A new statistic has been developed to quantify the amount of regularity in data. This statistic, ApEn (approximate entropy), appears to have potential application throughout medicine, notably in electrocardiogram and related heart rate data analyses and in the analysis of endocrine hormone release pulsatility. The focus of this article is ApEn. We commence with a simple example of what we are trying to discern. We then discuss exact regularity statistics and practical difficulties of using them in data analysis. The mathematic formula development for ApEn concludes the Solution section. We next discuss the two key input requirements, followed by an account of a pilot study successfully applying ApEn to neonatal heart rate analysis. We conclude with the important topic of ApEn as a relative (not absolute) measure, potential applications, and some caveats about appropriate usage of ApEn. Appendix A provides example ApEn and entropy computations to develop intuition about these measures. Appendix B contains a Fortran program for computing ApEn. This article can be read from at least three viewpoints. The practitioner who wishes to use a "black box" to measure regularity should concentrate on the exact formula, choices for the two input variables, potential applications, and caveats about appropriate usage. The physician who wishes to apply ApEn to heart rate analysis should particularly note the pilot study discussion. The more mathematically inclined reader will benefit from discussions of the relative (comparative) property of ApEn and from Appendix A.

A regularity statistic for medical data analysis

Objective The effect of colonoscopy on colorectal cancer mortality is limited by several factors, among them a certain miss rate, leading to limited adenoma detection rates (ADRs). We investigated the effect of an automatic polyp detection system based on deep learning on polyp detection rate and ADR. Design In an open, non-blinded trial, consecutive patients were prospectively randomised to undergo diagnostic colonoscopy with or without assistance of a real-time automatic polyp detection system providing a simultaneous visual notice and sound alarm on polyp detection. The primary outcome was ADR. Results Of 1058 patients included, 536 were randomised to standard colonoscopy, and 522 were randomised to colonoscopy with computer-aided diagnosis. The artificial intelligence (AI) system significantly increased ADR (29.1%vs20.3%, p Conclusions In a low prevalent ADR population, an automatic polyp detection system during colonoscopy resulted in a significant increase in the number of diminutive adenomas detected, as well as an increase in the rate of hyperplastic polyps. The cost–benefit ratio of such effects has to be determined further. Trial registration number ChiCTR-DDD-17012221; Results.

https://gut.bmj.com/content/gutjnl/68/10/1813.full.pdf

Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study.

This article deals with statistical region-based active contour segmentation using the alpha-divergence family as similarity measure between the density probability functions of the background and the object regions of interest. Following previous publications on that topic, main originality of this contribution is in the proposed joint optimization of the energy steering the evolution of the active curve and the parameter alpha related to the metric of the divergence and closely related to the statistical luminance distribution of the data. Experiments are shown on both synthetic noisy and textured data as well as on real images (natural and medical ones). We show that the joint optimization process leads to satisfying results for every targeted tasks: above all, it is shown that the proposed approach overcome classic statistical-based region active contour approach using Kullback-Leibler divergence as similarity measure, that can stuck in local extrema during the usual optimization process.

Statistical region-based active contour using optimization of alpha-divergence family for image segmentation

We propose to use Topological Data Analysis (TDA) to characterize the morphological development of brain organoids. We combine TDA with clustering strategies to characterize the morphology of three developmental stages of segmented brain organoid images. We calculate a linear regression of the H1 feature diagrams as well as entropy, dispersion, and average persistence of H0 and H1 features separately for each developmental stage. We also compute pairwise average Wasserstein distances between features, and within and inter clusters for each developmental stage. To explore all feature vectors from each group, we calculate K-L divergence on the t-SNE reduction of the TDA results. Early stages are characterized by a persistence diagram with a low slope and intercept, a high entropy within features, and a high Wasserstein distance between clusters near and far from the origin. The opposite is true for later stages. K-L divergences are particularly high between mid-stages and early or late stages, and punctually between some clusters. Results highlight specific morphological patterns at 14 days, corresponding to neuroepithelial formations. 

TDA-Clustering Strategies for the Characterization of Brain Organoids

In this article, our concern is the early diagnosis of colorectal cancer from a computeraided detection point of view in order to help physicians in their diagnosis during the gold standard examination: optical video colonoscopy. Since many years, some methods and materials have been developed to reduce the polyp miss rate and to improve detection capabilities. Nevertheless, the real challenge lies in the real-time use of these methods. In this context, more precisely, we focus our attention on the hardware implementation of a previous method we recently introduced in the literature for real-time detection of colorectal polyps, lesions that may degenerate into cancer. This implementation is subject to three performance criteria: real-time processing capabilities, detection rate and necessary computational resources. Six different platforms were tested and compared. If we noticed that only workstation computers are able to perform the detection with a good tradeoff between the three aforementioned criteria, possibilities of architecture optimizations are also identified and discussed in order to achieve real-time performance on platforms with low available computational resources like Raspberry Pi for instance. This latter issue is of major importance for possible integration of the detection algorithm inside smallconnected object like videocapsule, a promising alternative to standard colonoscopy.

Hardware Platforms Benchmark For Real-Time Polyp Detection

This book is the practical guide about designing, building and programming a modern robotics system It presents a collection of wide range research results from the robotics community Various aspects of current research in robotics area are explored and discussed In particular, the major part of the book is devoted to robotic vision systems, robot navigation, human-robot interaction and adaptive learning systems Different approaches include theorems and formal proofs for these systems are provided Some advanced mathematics will be involved This book is aimed at students and researchers who have an avid interest in this area will appreciate this user-friendly presentation of a wealth of robotics topics

Gradient Intensity Selectivity for Scalar Image Restoration using PDE

Pulse oximetry is one of the most commonly employed monitoring modalities in critical care setting. Conventional pulse oximeters use two leds at different wavelengths and a photodiode to estimate blood oxygen saturation noninvasively, based in the difference of absorption coefficients between hemoglobin and deoxyhemoglobin. Nevertheless, factors such as low oxygen saturations, skin melanin content, nail polish presence or led wavelength displacement, modify differently the expected light absorbance for both LEDs. To address these issues, a novel approach combining a single led and a Buried Quad Junction photodetector is proposed. With this fundamental modification of the pulse oximetry principle, errors associated with the aforementioned modifying effects are expected to be reduced. The preliminary results show that there is a correlation of 0.84, -0.93, 0.91 and -0.88 for channels 1, 2, 3 and 4 respectively between our proposed method and the response from the measuring reference.

Aymeric Histace

Papers

Statistical region-based active contour using optimization of alpha-divergence family for image segmentation

TDA-Clustering Strategies for the Characterization of Brain Organoids

Hardware Platforms Benchmark For Real-Time Polyp Detection

Gradient Intensity Selectivity for Scalar Image Restoration using PDE

Towards Spectral Pulse Oximetry Independent of Motion Artifacts