Parallelization Strategy for Elementary Morphological Operators on Graphs

International audienceThis article focuses on the graph-based mathematical morphology operators presented in [J. Cousty et al, Morphological ltering on graphs, CVIU 2013]. These operators depend on a size parameter that species the number of iterations of elementary dilations/erosions. Thus, the associated running times increase with the size parameter. In this article, we present distance maps that allow us to recover (by thresh-olding) all considered dilations and erosions. The algorithms based on distance maps allow the operators to be computed with a single linear-time iteration, without any dependence to the size parameter. Then, we investigate a parallelization strategy to compute these distance maps. The idea is to build iteratively the successive level-sets of the distance maps, each level set being traversed in parallel. Under some reasonable assumptions about the graph and sets to be dilated, our parallel algorithm runs in O(n/p + K log 2 p) where n, p, and K are the size of the graph, the number of available processors, and the number of distinct level-sets of the distance map, respectively

Learning System for Defactorization Factor Classification of Factorized Data Dependence Graph

International audienceIn the presence of a concrete problem of multi-objective optimization, we are confronting with the principal difficulty to choice a method producing the optimal solutions. This choice implies the knowledge and the expertise of the user. In this framework, we are interested to the flow of design based on methodology AAA (Adequacy Algorithm Architecture). The extension of this methodology to the circuits allows the exploitation of potential parallelism onto components. It aims to obtaining a real time implementation witch respect the temporal constraint of the application while minimizing the resources. Then, from an algorithm specified with a data flow graph, this exploration of parallelism is NP-complete problem. In this work, we propose a new solution to perform a multi-objective exploration by integrating an SVM (Support Vector Machine) training aptitude to an agent. We validate our model by a simulation based on the greedy heuristic results of SynDEX-IC (Synchronized Distributed Executive for Integrated circuit) [1] examples

Parallelization strategy for elementary morphological operators on graphs: distance-based algorithms and implementation on multicore shared-memory architecture

to appear in Journal of Mathematical Imaging and VisionInternational audienceThis article focuses on the (unweighted) graph-based mathematical morphology operators presented in [J. Cousty et al, " Morphological filtering on graphs " , CVIU 2013]. These operators depend on a size parameter that specifies the number of iterations of elementary dilations/erosions. Thus, the associated running times increase with the size parameter, the algorithms running in O(λ.n) time, where n is the size of the underlying graph and λ is the size parameter. In this article, we present distance maps that allow us to recover (by thresholding) all considered dilations and erosions. The algorithms based on distance maps allow the operators to be computed with a single linear O(n) time iteration, without any dependence to the size parameter. Then, we investigate a parallelization strategy to compute these distance maps. The idea is to build iteratively the successive level-sets of the distance maps, each level set being traversed in parallel. Under some reasonable assumptions about the graph and sets to be dilated, our parallel algorithm runs in O(n/p + K log 2 p) where n, p, and K are the size of the graph, the number of available processors, and the number of distinct level-sets of the distance map, respectively. Then, implementations of the proposed algorithm on a shared-memory multicore architecture are described and assessed on datasets of 45 images and 6 textured 3-dimensional meshes, showing a reduction of the processing time by a factor up to 55 over the previously available implementations on a 8 core architecture

Morphological operators on graph based on geodesic distance map

International audienc

Fully Automatic Brain Tumor Segmentation using End-to-End Incremental Deep Neural Networks in MRI images

International audienceBackground and Objective: Nowadays, getting an efficient Brain Tumor Segmentation in Multi-Sequence MR images as soon as possible, gives an early clinical diagnosis, treatment and follow-up. The aim of this study is to develop a new deep learning model for the segmentation of brain tumors. The proposed models are used to segment the brain tumors of Glioblastomas (with both high and low grade). Glioblastomas have four properties: different sizes, shapes, contrasts, in addition, Glioblastomas appear anywhere in the brain. Methods: In this paper, we propose three end-to-end Incremental Deep Convolutional Neural Networks models for fully automatic Brain Tumor Segmentation. Our proposed models are different from the other CNNs-based models that follow the technique of trial and error process which does not use any guided approach to get the suitable hyper-parameters. Moreover, we adopt the technique of Ensemble Learning to design a more efficient model. For solving the problem of training CNNs model, we propose a new training strategy which takes into account the most influencing hyper-parameters by bounding and setting a roof to these hyper-parameters to accelerate the training. Results: Our experiment results reported on BRATS-2017 dataset. The proposed deep learning models achieve the state-of-the-art performance without any post-processing operations. Indeed, our models achieve in average 0.88 Dice score over the complete region. Moreover, the efficient design with the advantage of GPU implementation, allows our three deep learning models to achieve brain segmentation results in average 20.87 seconds. Conclusions: The proposed deep learning models are effective for the segmentation of brain tumors and allow to obtain high accurate results. Moreover, the proposed models could help the physician experts to reduce the time of diagnostic

Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy

International audienceIn this paper, we present a new Deep Convolutional Neural Networks (CNNs) dedicated to fully automatic segmentation of Glioblastoma brain tumors with high-and low-grade. Where the proposed CNNs model is inspired by the Occipito-Temporal pathway which has a special function called selective attention that uses different receptive field sizes in successive layers to figure out the crucial objects in a scene. Thus, using selective attention technique to develop the CNNs model, helps to maximize the extraction of relevant features from MRI images. We have also treated two more issues: class-imbalance, and the spatial relationship among image Patches which are not addressed in the most state-of-the-art methods. To address the first issue, we propose two steps: equal sampling of images Patches and an experimental analysis of the effect of weighted cross-entropy loss function on the segmentation results. In addition, to overcome the second issue, we have studied the effect of Overlapping Patches against Adjacent Patches where the Overlapping Patches show a better segementation result due to the introduction of the global context as well as the local features of the image Patches compared to the conventionnel Adjacent Patches method. Our experiment results are reported on BRATS-2018 dataset where our End-to-End Deep Learning model achieved the state-of-the-art performance. The Mean Dice score of our fully automatic segmentation model is 0.86, 0.74, 0.74 for whole tumor, tumor core, and enhancing tumor respectively compared to the Dice score of radiologist that is in the range 74%-85%. Moreover, our proposed CNNs model not only computationally efficient at inference time, but it could segment the whole brain in average 16 seconds, in addition it has only 181,124 parameters. Finally, the proposed Deep Learning model provides an accurate and reliable segmentation result, and that make it suitable for adopting in research and as a part of different clinical settings

A left ventricular segmentation based on a parallel watershed transformation towards an accurate heart function evaluation

International audienceMagnetic Resonance Imaging (MRI) has emerged as the golden reference for cardiac examination. This modality allows the assessment of human cardiovascular morphology, functioning, and perfusion. Although a couple of challenging issues, such as the cardiac MR image's features and the large variability of images among several patients, still influences the cardiac cavities' segmentation and needs to be carried out. In this paper, we have profoundly reviewed and fully compared semi-automated segmentation methods performed on cardiac Cine-MR short-axis images for the evaluation of the left ventricular functions. However, the number of parameters handled by the synthesized works is limited if not null. For the sake of ensuring the highest coverage of the LV parameters computing, we have introduced a parallel watershed-based approach to segment the left ventricular allowing hence the computation of six parameters (End-Diastolic Volume, End-Systolic Volume, Ejection Fraction, Cardiac output, Stroke Volume and Left Ventricular Mass). An algorithm is associated with main considered measurements. The experimental results that were obtained through studying twenty patients' MRI data base, demonstrate the accuracy of our approach for estimating real values of the maximal set of parameters thanks to a faithful segmentation of the myocardium