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 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.
Highlights
X-ray CT and MRI are the most frequently used modalities for structural assessment in neurodegenerative disorders (Wattjes et al, 2009; Pasi et al, 2011)
We showed that 2D U-Nets could be successfully trained to perform automated segmentation of gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume in head CT
The boundary measures expressed by average HD (AHD), modified HD (MHD) were found to be (4.6, 1.67), (4.1, 1.19), and (4.43, 1.42) for GM, WM, and CSF, respectively
Summary
X-ray CT and MRI are the most frequently used modalities for structural assessment in neurodegenerative disorders (Wattjes et al, 2009; Pasi et al, 2011). MRI scans are commonly used for image-based tissue classification to quantify and extract atrophy-related measures from structural neuroimaging modalities (Despotovicet al., 2015). CT scanning is used for the visual assessment of brain integrity and the exclusion of copathologies in neurodegenerative diseases (Musicco et al, 2004; Rayment et al, 2016). In comparison with MR imaging, CT scanning is faster, cheaper, and more widely available. Despite these advantages, automated tissue classification in head CT is largely underexplored
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