An end-end deep learning framework for lesion segmentation on multi-contrast MR images-an exploratory study in a rat model of traumatic brain injury.

Traumatic brain injury (TBI) engenders traumatic necrosis and penumbra-areas of secondary neural injury which are crucial targets for therapeutic interventions. Segmenting manually areas of ongoing changes like necrosis, edema, hematoma, and inflammation is tedious, error-prone, and biased. Using the multi-parametric MR data from a rodent model study, we demonstrate the effectiveness of an end-end deep learning global-attention-based UNet (GA-UNet) framework for automatic segmentation and quantification of TBI lesions.

Amyloid pathology fingerprint differentiates post-traumatic stress disorder and traumatic brain injury.

Introduction: Traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD) are risk factors for early onset of Alzheimer's disease (AD) and may accelerate the progression rate of AD pathology. As amyloid-beta (Aβ) plaques are a hallmark of AD pathology, we hypothesized that TBI and PTSD might increase Aβ accumulation in the brain.

Methods: We examined PET and neuropsychological data from Vietnam War veterans compiled by the US Department of Defense Alzheimer's Disease Neuroimaging Initiative, to examine the spatial distribution of Aβ in male veterans' who had experienced a TBI and/or developed PTSD.

A method for the automatic segmentation of brown adipose tissue.

Objective: Brown adipose tissue (BAT) plays a key role for thermogenesis in mammals and infants. Recent confirmation of BAT presence in adult humans has aroused great interest for its potential to initiate weight-loss and normalize metabolic disorders in diabetes and obesity. Reliable detection and differentiation of BAT from the surrounding white adipose tissue (WAT) and muscle is critical for assessment/quantification of BAT volume.