Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT

Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT

Brain tissue segmentation plays a crucial role in feature extraction, volumetric quantification, and morphometric analysis of brain scans. For the assessment of brain structure and integrity, CT is a non-invasive, cheaper, faster, and more widely available modality than MRI. However, the clinical ap...

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Bibliographic Details
Main Authors: Meera Srikrishna, Rolf A. Heckemann, Joana B. Pereira, Giovanni Volpe, Anna Zettergren, Silke Kern, Eric Westman, Ingmar Skoog, Michael Schöll
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-01-01
Series:Frontiers in Computational Neuroscience
Subjects:
brain image segmentation
CT
MRI
deep learning
convolutional neural networks
Online Access:https://www.frontiersin.org/articles/10.3389/fncom.2021.785244/full

Internet

https://www.frontiersin.org/articles/10.3389/fncom.2021.785244/full

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