BACKGROUND AND PURPOSE: Our aim was to use 2D convolutional neural networks for
automatic segmentation of the spinal cord and traumatic contusion injury from axial T2 …

It is challenging to predict neurological outcomes of acute spinal cord injury (SCI)
considering issues such as spinal shock and injury heterogeneity. Deep learning-based …

Background In patients with chronic spinal cord injury, imaging of the spinal cord and brain
above the level of the lesion provides evidence of neural degeneration; however, the spatial …

Magnetic resonance imaging (MRI) can indirectly reflect microscopic changes in lesions on
the spinal cord; however, the application of deep learning to MRI to classify and detect …

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS)
patients. Segmentation of the spinal cord and lesions from MRI data provides measures of …

Detection of spinal cord injury (SCI) is one of the major problems in MRI images to detect the
affected portion of spinal cord regions using feature sets. Automatic detection of spinal cord …

CT has a higher sensitivity to fractures (especially involving the posterior elements) than
radiography. This rapid cross-sectional imaging assessment of the spinal axis has been …

Objective To reveal the immediate extent of trauma-induced neurodegenerative changes
rostral to the level of lesion and determine the predictive clinical value of quantitative MRI …

Traumatic spinal cord injury occurs when an external physical impact damages the spinal
cord and leads to permanent neurological dysfunction and disability, and it is associated …

Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain
morphometric quantification (size, location, growth rate) of the tumor, edema, and cavity can …