Efficient segmentation of active and inactive plaques in FLAIR-images using DeepLabV3Plus SE with efficientnetb0 backbone in multiple sclerosis.

This research paper introduces an efficient approach for the segmentation of active and inactive plaques within Fluid-attenuated inversion recovery (FLAIR) images, employing a convolutional neural network (CNN) model known as DeepLabV3Plus SE with the EfficientNetB0 backbone in Multiple sclerosis (MS), and demonstrates its superior performance compared to other CNN architectures. The study encompasses various critical components, including dataset pre-processing techniques, the utilization of the Squeeze and Excitation Network (SE-Block), and the atrous spatial separable pyramid Block to enhance segmentation capabilities. Detailed descriptions of pre-processing procedures, such as removing the cranial bone segment, image resizing, and normalization, are provided.

RehaCom rehabilitation training improves a wide-range of cognitive functions in multiple sclerosis patients.

Multiple sclerosis (MS) is a chronic neurodegenerative disease that impairs cognitive performance. Attention, response control, working memory, and processing speed are highly impaired in MS. On the other hand, RehaCom is a computerized software that improves cognitive dysfunctions.

The therapeutic effect of treatment with RehaCom software on verbal performance in patients with multiple sclerosis.

Multiple sclerosis (MS) is characterized by central nervous system lesions that lead to neurological dysfunctions including fatigue, depression and anxiety. MS is affecting almost 2.3 million people around the world, with the significant highest prevalence in the North America.