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

Photoplethysmography and its application in clinical physiological measurement

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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 paper presents an FPGA-based algorithm for automatic detection of QRS complexes in ECG signals. The proposed algorithm is divided into 3 blocks: Filtering, Contrast Enhancement, and finally a Detection block based on time and amplitude thresholding of the enhanced data. The entire detection scheme was developed in accordance with embedding constraints. For validation, the method was tested on the MIT/BIH arrhythmia database containing 48 records of 30-minutes ECG. The FPGA-based algorithm correctly detects 95,35 percent of the QRS complexes, with a very limited ratio of false detection (only 8%). These results are in accordance with the most recent state-of-the-art off-line algorithms on the same database, and improves significantly FPGA-based ones that were tested on a limited number of ECG extracted from the MIT/BIH set of data.

FPGA-based detection of QRS complexes in ECG signal

Most image retrieval methods use global features that aggregate local distinctive patterns into a single representation. However, the aggregation process destroys the relative spatial information by considering orderless sets of local descriptors. We propose to integrate relative spatial information into the aggregation process by taking into account co-occurrences of local patterns in a tensor framework. The resulting signature called Improved Spatial Tensor Aggregation (ISTA) is able to reach state of the art performances on well known datasets such as Holidays, Oxford5k and Paris6k.

Leveraging Implicit Spatial Information in Global Features for Image Retrieval

Anisotropic image restoration based on Partial Differential Equation (PDE) is of major interest for image regularization with preservation of the discontinuities. Applications are various and deal with numerous areas of image processing (photography, medical application, etc.). In this article we propose a variational approach for image restoration based on the minimization of an energetic function different from those encountered in the literature. The resulting PDE is characterized by the possible integration of a priori information on particular structures to restore during the diffusion process. We first present results obtained on ad hoc images, and then we show a possible extension of this research work to a particular application: the restoration of tagged cardiac MRI sequences.

Restauration d'images par diffusion sélective

Pulse oximetry is one of the most commonly employed monitoring modalities in critical care setting. Conventional pulse oximeters, use two leds and a photodiode to estimate the percentage of oxyhemoglobin and deoxyhemoglobin, assuming that those are the only light absorbers in the blood. Nevertheless, the presence of other types of dyshemoglobin may invalidate their calibration. In this study, we employed a Buried Quad Junction photodetector in order to estimate the oxygen saturation using simultaneously lit leds. More precisely, a proof-of-principle was shown for developing an oximeter capable of estimating up to four types of hemoglobin simultaneously with a single led. Results from in vivo measurements obtained with this setup show that we successfully isolated the information of our interest for oxygen saturation estimation. These results were compared with a commercial device yielding acceptable estimations with a mean error of 1.3% of the oxygen saturation estimation.

Prototype of a multispectral and multiwavelength pulse oximeter based on a BQJ sensor

Background: Bubbles often mask the mucosa during capsule endoscopy (CE). Clinical scores assessing the cleanliness and the amount of bubbles in the small bowel (SB) are poorly reproducible unlike machine learning (ML) solutions. We aimed to measure the amount of bubbles with ML algorithms in SB CE recordings, and compare two polyethylene glycol (PEG)-based preparations, with and without simethicone, in patients with obscure gastro-intestinal bleeding (OGIB). Patients & Methods: All consecutive outpatients with OGIB from a tertiary care center received a PEG-based preparation, without or with simethicone, in two different periods. The primary outcome was a difference in the proportions (%) of frames with abundant bubbles (>10%) along the full-length video sequences between the two periods. SB CE recordings were analyzed by a validated computed algorithm based on a grey-level of co-occurrence matrix (GLCM), to assess the abundance of bubbles in each frame. Results: In total, 105 third generation SB CE recordings were analyzed (48 without simethicone and 57 with simethicone-added preparations). A significant association was shown between the use of a simethicone-added preparation and a lower abundance of bubbles along the SB (p = 0.04). A significantly lower proportion of “abundant in bubbles” frames was observed in the fourth quartile (30.5% vs. 20.6%, p = 0.02). There was no significant impact of the use of simethicone in terms of diagnostic yield, SB transit time and completion rate. Conclusion: An accurate and reproducible computed algorithm demonstrated significant decrease in the abundance of bubbles along SB CE recordings, with a marked effect in the last quartile, in patients for whom simethicone had been added in PEG-based preparations, compared to those without simethicone.

/pdf/evaluation-by-a-machine-learning-system-of-two-preparations-2mbh4k7g.pdf

Evaluation by a Machine Learning System of Two Preparations for Small Bowel Capsule Endoscopy: The BUBS (Burst Unpleasant Bubbles with Simethicone) Study

Aymeric Histace

Papers

FPGA-based detection of QRS complexes in ECG signal

Leveraging Implicit Spatial Information in Global Features for Image Retrieval

Restauration d'images par diffusion sélective

Prototype of a multispectral and multiwavelength pulse oximeter based on a BQJ sensor

Evaluation by a Machine Learning System of Two Preparations for Small Bowel Capsule Endoscopy: The BUBS (Burst Unpleasant Bubbles with Simethicone) Study