A 2.5D Self-Training Strategy for Carotid Artery Segmentation in T1-Weighted Brain Magnetic Resonance Images.

Precise annotations for large medical image datasets can be time-consuming. Additionally, when dealing with volumetric regions of interest, it is typical to apply segmentation techniques on 2D slices, compromising important information for accurately segmenting 3D structures. This study presents a deep learning pipeline that simultaneously tackles both challenges.

Conventional Mirror Therapy versus Immersive Virtual Reality Mirror Therapy: The Perceived Usability after Stroke.

Background: Stroke is a widespread and complex health issue, with many survivors requiring long-term rehabilitation due to upper-limb impairment. This study is aimed at comparing the perceived usability of two feedback-based stroke therapies: conventional mirror therapy (MT) and immersive virtual reality mirror therapy (VR).

Methods: The study involved 45 participants, divided into three groups: the stroke survivors ( = 15), stroke-free older adults ( = 15), and young controls ( = 15).

Verbal Learning as a predictor of risks of accidents in elderly drivers.

Background: Age-related cognitive decline impacts cognitive abilities essential for driving.

Objective: We aimed to measure main cognitive functions associated with a high number of traffic violations in different driving settings.

Methods: Thirty-four elderly individuals, aged between 65 and 90 years, were evaluated with a driving simulator in four different settings (Intersection, Overtaking, Rain, and Malfunction tasks) and underwent a battery of cognitive tests, including memory, attention, visuospatial, and cognitive screening tests.

An algorithm to track laboratory zebrafish shoals.

In this paper, a semi-automatic multi-object tracking method to track a group of unmarked zebrafish is proposed. This method can handle partial occlusion cases, maintaining the correct identity of each individual. For every object, we extracted a set of geometric features to be used in the two main stages of the algorithm.

Atlas selection for hippocampus segmentation: Relevance evaluation of three meta-information parameters.

Current state-of-the-art methods for whole and subfield hippocampus segmentation use pre-segmented templates, also known as atlases, in the pre-processing stages. Typically, the input image is registered to the template, which provides prior information for the segmentation process. Using a single standard atlas increases the difficulty in dealing with individuals who have a brain anatomy that is morphologically different from the atlas, especially in older brains.

Automated methods for hippocampus segmentation: the evolution and a review of the state of the art.

The segmentation of the hippocampus in Magnetic Resonance Imaging (MRI) has been an important procedure to diagnose and monitor several clinical situations. The precise delineation of the borders of this brain structure makes it possible to obtain a measure of the volume and estimate its shape, which can be used to diagnose some diseases, such as Alzheimer's disease, schizophrenia and epilepsy. As the manual segmentation procedure in three-dimensional images is highly time consuming and the reproducibility is low, automated methods introduce substantial gains.