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COVID-19 originally started in Wuhan city in China. The disease rapidly became a worldwide pandemic, causing a respiratory illness with symptoms such as coughing, fever, and in more severe cases difficulty in breathing. With the current testing processes, it is very difficult and sometimes impossible to manage and provide the necessary treatment to suspected patients since the number of the infected is rapidly increasing. Hence, the availability of an artificial intelligent driven system can be an assistive tool to provide accurate diagnosis using radiology imaging techniques. In this paper, we put forward a new deep learning architecture, which integrates the Nested Residual Connections (NRCs) in a DarkCovidNet model, called DarkCovidNet-NRC, in order to classify chest images and to detect COVID-19 cases. The proposed architecture is validated with the K-fold cross-validation technique on X-ray and CT chest datasets separately and then combined. The experimental results reveal that the suggested model performs very well in the medical classification task and it competes with the state of the art in multiple performance metrics by respectively achieving an accuracy and precision of 0.9609 and 0.978 on the combined dataset.

Segmentation of cardiac magnetic resonance images (cMRI) remains a challenging task in the field of scientific research due to its significance in the medical assessment of cardiovascular diseases. Ensuring accurate segmentation of the heart structures, mainly the left ventricle cavity, serves to extract important information and has a major impact on the quantitative analysis of the heart function which helps to conduct the proper diagnosis of doctors. The present paper introduces a simple and efficient U-shaped convolutional neural network aiming to accurately segment the LV from cMR images. We applied our architecture for Left Ventricle (LV) segmentation on cardiac MR images (cMRI), from the Automated Cardiac Diagnosis Challenge (ACDC). Obtained results are promising. This simple model based on CNN has significantly fewer parameters rendering it less demanding in terms of computation. Nevertheless, it has provided accurate segmentation. The tested method achieved LV Dice scores of 0.958 at end-systolic time (ES) and 0.979 at end-diastolic time (ED), which yields a mean Dice score of 0.968 on the ACDC dataset.

International audience; The right ventricle (RV) is the largest heart cavity playing a vital role in the cardiac cycle and function. However, to assess its function in cardiac magnetic resonance imaging (CMRI), the segmentation is an essential but very challenging task due to the RV complex crescent shape, thin boundaries, as well as the high variability of images. Indeed, to overcome these issues, several approaches have been proposed. In this paper, we aim to highlight the impact of various short-axis slices on the segmentation process. For that sake, based on the Cvi42 advanced deep learning base software, we assess the effectiveness of the RV contour delineation process over an entire MRI cardiac short-axis slice. We consider for evaluating the accuracy, the Dice Score Metric (DSM), as well as some RV functional parameters such as the Ejection Fraction, the End-Systolic-Volume, and the End-Diastolic-Volume. Our experimental assessment is done using two CMRI exams of both healthy and diseased patients. We consider for evaluation the whole short-axis frames from base to apex and from end-systole to end-diastole. The evaluation results show that: (1) The segmentation accuracy is influenced by the slice level especially on the basal and apical slices, (2) A huge accuracy decreasing for the case of a patient having dysplasia.

International audience; The current challenge for image treatment, targeting the heart ventricles (LV and RV), consists on ensuring an accurate automatic segmentation aiming to identify the myocardial contours. In this paper we propose an automatic watershed-based segmentation of the left ventricle (LV)based on topology, geometry and brightness priors. We attempted to demonstrate the accuracy of our proposed approach by comparing it with other manual segmentations carried out by two clinical experts, on 20 patients. In the aim of obtaining an accurate 3D reconstruction, we propose the volumetric object reconstruction method using The Visualization Toolkit VTK .Furthermore, the 3D+t assessment is used for the examination of the dynamic behavior of the LV to reveal regions of myocardial dysfunction.

This research is based on a hemodynamic study of liver donor patient’s normal venous blood flow. The aim is to build a 3D model of the venous system, in order to visualize the blood pressure’s variation as well as its velocity in the liver through a CFD (Computational Fluid Dynamics) study. In this case, we are going to propose an upcoming study explaining the phenomenon of portal hypertension of the receiver’s liver patient which led to his suffering from Esophageal varices.

The aim of this study is the microcalcification segmentation in digital mammograms. We propose two different methods which are based on the combination of the multifractal analysis with, respectively, the fractal analysis and then with the lacunarity. Our approach consists of two steps. On the first stage, we created the “α_image.” This image was constructed by singularity coefficient deduced from multifractal spectrum of the original image. On the second stage, in order to enhance the visualization of microcalcifications, we create the “f(α)_image” based on global regularity measure of “α_image.” Two different techniques are used: the box counting (BC) used to calculate fractal dimension and the gliding box method used to measure lacunarity. These techniques were applied in order to compare results. Our proposed approaches were tested on mammograms from “MiniMIAS database.” Results demonstrate that microcalcifications were successfully segmented.

One of the major concerns in cardiac imaging is finding an efficient method for the deformation analysis in the left ventricle of the heart (LV). This paper deals with the results of using a regional analysis of left ventricular of the heart applied on MRI data. Our analysis consists of segmenting 3D objects modeling the myocardium of the LV into 17 regions according to the AHA (American Heart Association) standard. Curvature variation was calculated using the Hotelling MT2 two samples difference metric for the global objects and their different 17 regions during 25 instants of the cardiac cycle. It has been validated with real MRI data. Experimental results demonstrate the great robustness and efficiency of our method to detect pathological regions of myocardium.

X-ray Coronary angiograms are intended to specify the global state of the artery system and therefore to detect and locate the zones of narrowing. Accurate coronary artery segmentation is a fundamental step in computer aided diagnosis of many diseases. In this paper, deep neural network based on U-Net architecture is proposed in order to improve the segmentation task for coronary images. In this context, various enhancement methods, like the adaptive histogram equalization and the multiscale technique with a Frangi filter are tested not only in normal conditions but also in the presence of noise to improve the system performance and to ensure its robustness against real conditions. Promising result are obtained and discussed for different performance criteria. This work will serve as a reference and motivation for researchers interested in the field of blood vessel segmentation by deep learning neural networks.

In this paper we propose a new multi-scale fully automatic algorithm based on Graph cuts for vessel extraction. In fact, we combine vesselness, geodesic paths, a multi-scale edgeness map and the directional information for vessel tracking in order to personalize the Graph cuts approach to the segmentation of tubular structures.

Recently, research into pathological cytology were intended to put in places of artificial intelligence systems based on the development of new diagnostic technologies and the cell image segmentation. These technologies are not intended to substitute the human expert but to facilitate his task. The objective of this work is to develop a method for diagnosing cancer cervical smears using cervical - vaginal segmented to build our database and a human supervisor and as an automatic tool manage and monitor the execution of the operation of diagnostic and proposing corrective actions if necessary. The Supervisor Smart is manufactured by the technique of neural networks with a success rate of 43.3% followed by the technique of fuzzy logic with a success rate equal to 56.7% and finally to improve this rate we used neuro-fuzzy approach which has a rate which reaches 94%.