12 research outputs found
Comparative Microbicidal Efficacy of Fractionated Extracts from In Vitro and In Vivo Raised Cells of Tinosporacordifolia Against MDR Pathogens
A regular curd consumption improves gastrointestinal status assessed by a randomized controlled nutritional intervention
SIRS as a predictor of poorer outcomes in diabetic foot infection
This was a single centre cohort study, in which 50 consecutive DFI patients having SIRS and 50 consecutive patients not having SIRS were included. Patients were followed for the duration of the hospital stay; parameters for glycaemic control, minor & major amputation, microbial culture, duration of hospital and ICU stay and mortality was recorded
An Experimental Investigation on the Spray Flows Exhausted from a Counter-swirling Air-blast Nozzle
Comparison of Endotracheal Intubation Through I-gel and Intubating Laryngeal Mask Airway
Influence of Self-excited Vibrating Cavity Structure on Droplet Diameter Characteristics of Twin-fluid Nozzle
Re-defining the anatomical structures for blocking the nerves in adductor canal and sciatic nerve through the same injection site: an anatomical study
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
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 tumor is 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 the state-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 total resection. 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