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Found 5 papers, 2 papers with code
Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networks
In this paper, we propose a fully convolutional neural network (CNN) based method to segment white matter lesions from multi-contrast MR images.
Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks
The goal of this study was to develop a fully-automatic framework, robust to variability in both image parameters and clinical condition, for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data.
Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI
The automated detection of cortical lesions (CLs) in patients with multiple sclerosis (MS) is a challenging task that, despite its clinical relevance, has received very little attention.
Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis
Deep learning approaches to the segmentation of magnetic resonance images have shown significant promise in automating the quantitative analysis of brain images.
Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?
Segmentation of Multiple Sclerosis (MS) lesions is a challenging problem.
