Correction: Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals.

[This corrects the article DOI: 10.1371/journal.pone.

Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals.

Severe spinal cord injury (SCI) leads to skeletal muscle atrophy and adipose tissue infiltration in the skeletal muscle, which can result in compromised muscle mechanical output and lead to health-related complications. In this study, we developed a novel automatic 3-D approach for volumetric segmentation and quantitative assessment of thigh Magnetic Resonance Imaging (MRI) volumes in individuals with chronic SCI as well as non-disabled individuals. In this framework, subcutaneous adipose tissue, inter-muscular adipose tissue and total muscle tissue are segmented using linear combination of discrete Gaussians algorithm.

Potential Alternative Treatment of Ocular Bacterial Infections by Oil Derived from Syzygium aromaticum Flower (Clove).

Unlabelled: Purpose/Aim: This study aims to evaluate the predisposing risk factors and antibiotic resistance of bacterial corneal ulcer to commonly used antibiotics. In addition, assess the in vitro efficacy of plant-derived essential oils (EOs) as safe and effective antimicrobial agents.

Methods: Demographic features and predisposing risk factors of corneal ulcer patients were recorded.

An automated approach for early detection of diabetic retinopathy using SD-OCT images.

This study was to demonstrate the feasibility of an automatic approach for early detection of diabetic retinopathy (DR) from SD-OCT images. These scans were prospectively collected from 200 subjects through the fovea then were automatically segmented, into 12 layers. Each layer was characterized by its thickness, tortuosity, and normalized reflectivity.

Alzheimer's disease diagnostics by a 3D deeply supervised adaptable convolutional network.

Early diagnosis is playing an important role in preventing progress of the Alzheimer's disease (AD). This paper proposes to improve the prediction of AD with a deep 3D Convolutional Neural Network (3D-CNN), which can show generic features capturing AD biomarkers extracted from brain images, adapt to different domain datasets, and accurately classify subjects with improved fine-tuning method. The 3D-CNN is built upon a convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans for source domain.