Showing papers by "Aymeric Histace published in 2017"

Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

Abstract: Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge ( http://endovis.grand-challenge.org ) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance.

Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis

Abstract: Colorectal cancer is the second cause of cancer death in United States: precursor lesions (polyps) detection is key for patient survival. Though colonoscopy is the gold standard screening tool, some polyps are still missed. Several computational systems have been proposed but none of them are used in the clinical room mainly due to computational constraints. Besides, most of them are built over still frame databases, decreasing their performance on video analysis due to the lack of output stability and not coping with associated variability on image quality and polyp appearance. We propose a strategy to adapt these methods to video analysis by adding a spatio-temporal stability module and studying a combination of features to capture polyp appearance variability. We validate our strategy, incorporated on a real-time detection method, on a public video database. Resulting method detects all polyps under real time constraints, increasing its performance due to our adaptation strategy.

A study on the limitations of a vane rheometer for mineral suspensions using image processing

Abstract: This study presents the results from the rheological measurement of clay suspensions using vane geometry in a wide gap configuration. It focuses on how measurement of viscosity cannot be effective for two reasons: the limits of the vane geometry itself and the limits of the material depending on its content of solid particles. Image analysis of the flow while shearing the material is carried out to relate the flow behavior. Several approaches to compute the shear flow curve from torque-rotational velocity data are used. The results demonstrate that the applied setpoint while applying a logarithmic shear rate ramp can be very different from the calculated shear rate from existing theories. Depending on the solid volume fraction of the particles in the mixture, we relate the macroscopic behavior using image analysis and the shear flow curves to the rheophysical regime of the flow of the suspensions. Therefore, this paper has two simultaneous goals: the first one is to describe the physical phenomena which control macroscopic behavior and the second one is to highlight the limits of the vane geometry for viscosity measurement of mineral suspensions like kaolinite pastes.

Method and apparatus for real-time detection of polyps in optical colonoscopy

Abstract: A method for performing real-time detection and displaying of polyps in optical colonoscopy, includes a) acquiring and displaying a plurality of real-time images within colon regions to a video stream frame rate, each real-time image comprising a plurality of color channels; b) selecting one single color channel per real-time image for obtaining single color pixels; c) scanning the single color pixels across each the real-time image with a sliding sub-window; d) for each position of the sliding sub-window, extracting a plurality of single color pixels local features of the real-time image; e) passing the extracted single color pixels local features of the real-time image through a classifier to determine if a polyp is present within the sliding sub-window; f) real-time framing on display of colon regions corresponding to positions of the sliding sub-window wherein polyps are detected. A system for carrying out such a method is also provided.

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

Abstract: 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.

Hardware Platforms Benchmark For Real-Time Polyp Detection

Abstract: 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.

Embeddable Real Time Tool for Automatic Skin Lesions Characterization

Abstract: In this paper, we propose a new method to detect and characterize nevi in real time. After some facts and figures about the skin cancer, we introduce our method. Then, we show how this method is developed and works (using machine learning and image processing). After, we present the results of the performances of this method in terms of detection and characterization. And to finish, we conclude about this method and its results.