University of Technology of Troyes

About: University of Technology of Troyes is a education organization based out in Troyes, Champagne-Ardenne, France. It is known for research contribution in the topics: Plasmon & Vehicle routing problem. The organization has 1325 authors who have published 3471 publications receiving 68791 citations.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Nanostructured surface layer on metallic materials induced by surface mechanical attrition treatment

Abstract: In terms of the grain refinement mechanism induced by plastic straining, a novel surface mechanical attrition treatment (SMAT) was developed for synthesizing a nanostructured surface layer on metallic materials in order to upgrade the overall properties and performance. In this paper, the SMAT technique and the microstructure of the SMAT surface layer will be described. The grain refinement mechanism of the surface layer during the SMAT will be analyzed in terms of the microstructure observations in several typical materials. Obvious enhancements in mechanical properties and tribological properties of the nanostructured surface layer in different materials were observed. Further development and prospects will be addressed with respect to the SMAT as well as the performance and technological applications of the engineering materials with the nanostructured surface layer.