QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g.

Ergonomics factors influencing school education during the COVID-19 pandemic: A literature review.

Background: The school is one of the most critical social, educational, and training institutions and the main pillar of education in society. Education and, consequently, educational environments have the highest effect on the mentality, development, growth, welfare, concentration, performance, and learning efficiency of students.

Objectives: The present study aimed to examine the effects of environmental ergonomics on the learning and cognition of pre-school students during the COVID-19 pandemic.

The prevalence of metabolic syndrome in drivers: A meta-analysis and systematic review.

Background: Metabolic syndrome is an increasing disorder, especially in night workers. Drivers are considered to work during 24 hours a day. Because of job characteristics such as stress, low mobility and long working hours, they are at risk of a metabolic syndrome disorder.

Shift working disorders among nurses of Tehran hospital and its related factors in 2016.

Introduction: Many adverse effects occur among the nurses due to shift work Hence, the present study aimed to determine the prevalence of shift work-related disorders and its related factor among the nurses at Tehran University Subsidiary Hospital, Iran, and to find solutions for managing the relevant health problems.

Methods: In this cross-sectional study, the Survey of Shift workers (SOS) questionnaire and the Personal Information Form were used to collect data related to demographics and working conditions of 1259 randomly selected nurses working at Tehran University Subsidiary Hospital as statistical population.

Results: According to the results, psychological disorders (95%), digestive problems (85%) and social problems (80%) were the most frequent problems among the subjects.

Three Dimensional Root CT Segmentation using Multi-Resolution Encoder-Decoder Networks.

We address the complex problem of reliably segmenting root structure from soil in X-ray Computed Tomography (CT) images. We utilise a deep learning approach, and propose a state-of-the-art multi-resolution architecture based on encoderdecoders. While previous work in encoder-decoders implies the use of multiple resolutions simply by downsampling and upsampling images, we make this process explicit, with branches of the network tasked separately with obtaining local high-resolution segmentation, and wider low-resolution contextual information.

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

Background: Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images.

Methods: We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI).

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.

Purpose: We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI).

Methods: The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification.