Deep learning segmentation of orbital fat to calibrate conventional MRI for longitudinal studies

Volume: 208, Pages: 116442 - 116442
Published: Mar 1, 2020
Abstract
In conventional non-quantitative magnetic resonance imaging, image contrast is consistent within images, but absolute intensity can vary arbitrarily between scans. For quantitative analysis of intensity data, images are typically normalized to a consistent reference. The most convenient reference is a tissue that is always present in the image, and is unlikely to be affected by pathological processes. In multiple sclerosis neuroimaging, both the...
Paper Details
Title
Deep learning segmentation of orbital fat to calibrate conventional MRI for longitudinal studies
Published Date
Mar 1, 2020
Journal
Volume
208
Pages
116442 - 116442
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