Keywords: Analysis/Processing, Segmentation
Motivation: While computed tomography (CT) imaging has been actively employed in clinical practice, its limited contrast for brain tissues makes it challenging to achieve precise brain segmentation.
Goal(s): In this study, we developed a deep learning (DL)-based method enabling brain tissue segmentation from CT image.
Approach: MRI-derived tissue labels were provided as ground truth to a DL network, where U-Net and VGG16 interact to each other for model optimization by means of a perceptual loss.
Results: Results demonstrate the effectiveness of incorporating the perceptual loss to the model in preserving image details, and in terms of evaluation scores.
Impact: The presented method, upon further validation and optimization, is expected to be a valuable means to a range of brain imaging studies where MRI is somehow not available.
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