Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT
(2022) In Frontiers in Computational Neuroscience 15.- Abstract
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 application of CT is mostly limited to the visual assessment of brain integrity and exclusion of copathologies. We have previously developed two-dimensional (2D) deep learning-based segmentation networks that successfully classified brain tissue in head CT. Recently, deep learning-based MRI segmentation models successfully use patch-based three-dimensional (3D) segmentation networks. In this study, we aimed to develop patch-based 3D... (More)
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 application of CT is mostly limited to the visual assessment of brain integrity and exclusion of copathologies. We have previously developed two-dimensional (2D) deep learning-based segmentation networks that successfully classified brain tissue in head CT. Recently, deep learning-based MRI segmentation models successfully use patch-based three-dimensional (3D) segmentation networks. In this study, we aimed to develop patch-based 3D segmentation networks for CT brain tissue classification. Furthermore, we aimed to compare the performance of 2D- and 3D-based segmentation networks to perform brain tissue classification in anisotropic CT scans. For this purpose, we developed 2D and 3D U-Net-based deep learning models that were trained and validated on MR-derived segmentations from scans of 744 participants of the Gothenburg H70 Cohort with both CT and T1-weighted MRI scans acquired timely close to each other. Segmentation performance of both 2D and 3D models was evaluated on 234 unseen datasets using measures of distance, spatial similarity, and tissue volume. Single-task slice-wise processed 2D U-Nets performed better than multitask patch-based 3D U-Nets in CT brain tissue classification. These findings provide support to the use of 2D U-Nets to segment brain tissue in one-dimensional (1D) CT. This could increase the application of CT to detect brain abnormalities in clinical settings.
(Less)
- author
- Srikrishna, Meera ; Heckemann, Rolf A. ; Pereira, Joana B. LU ; Volpe, Giovanni ; Zettergren, Anna ; Kern, Silke ; Westman, Eric ; Skoog, Ingmar and Schöll, Michael LU
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- brain image segmentation, convolutional neural networks, CT, deep learning, MRI
- in
- Frontiers in Computational Neuroscience
- volume
- 15
- article number
- 785244
- publisher
- Frontiers Media S. A.
- external identifiers
-
- scopus:85123384309
- pmid:35082608
- ISSN
- 1662-5188
- DOI
- 10.3389/fncom.2021.785244
- language
- English
- LU publication?
- yes
- id
- f8df6075-b3f5-415e-80e8-a62776e48f1e
- date added to LUP
- 2022-03-18 15:32:28
- date last changed
- 2024-04-18 06:30:23
@article{f8df6075-b3f5-415e-80e8-a62776e48f1e,
abstract = {{<p>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 application of CT is mostly limited to the visual assessment of brain integrity and exclusion of copathologies. We have previously developed two-dimensional (2D) deep learning-based segmentation networks that successfully classified brain tissue in head CT. Recently, deep learning-based MRI segmentation models successfully use patch-based three-dimensional (3D) segmentation networks. In this study, we aimed to develop patch-based 3D segmentation networks for CT brain tissue classification. Furthermore, we aimed to compare the performance of 2D- and 3D-based segmentation networks to perform brain tissue classification in anisotropic CT scans. For this purpose, we developed 2D and 3D U-Net-based deep learning models that were trained and validated on MR-derived segmentations from scans of 744 participants of the Gothenburg H70 Cohort with both CT and T1-weighted MRI scans acquired timely close to each other. Segmentation performance of both 2D and 3D models was evaluated on 234 unseen datasets using measures of distance, spatial similarity, and tissue volume. Single-task slice-wise processed 2D U-Nets performed better than multitask patch-based 3D U-Nets in CT brain tissue classification. These findings provide support to the use of 2D U-Nets to segment brain tissue in one-dimensional (1D) CT. This could increase the application of CT to detect brain abnormalities in clinical settings.</p>}},
author = {{Srikrishna, Meera and Heckemann, Rolf A. and Pereira, Joana B. and Volpe, Giovanni and Zettergren, Anna and Kern, Silke and Westman, Eric and Skoog, Ingmar and Schöll, Michael}},
issn = {{1662-5188}},
keywords = {{brain image segmentation; convolutional neural networks; CT; deep learning; MRI}},
language = {{eng}},
publisher = {{Frontiers Media S. A.}},
series = {{Frontiers in Computational Neuroscience}},
title = {{Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT}},
url = {{http://dx.doi.org/10.3389/fncom.2021.785244}},
doi = {{10.3389/fncom.2021.785244}},
volume = {{15}},
year = {{2022}},
}