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Dual energy CT and deep learning for an automated volumetric segmentation of the major intracranial tissues : Feasibility and initial findings

Fransson, Veronica LU orcid ; Winzell, Filip LU ; Ramgren, Birgitta LU ; Christensen, Søren LU ; Ydström, Kristina LU ; Arvidsson, Ida LU orcid ; Overgaard, Niels Christian LU ; Åström, Kalle LU orcid ; Heyden, Anders LU orcid and Wassélius, Johan LU (2026) In Medical Physics 53(1).
Abstract

Background: Magnetic resonance imaging (MRI) has traditionally been preferred over computed tomography (CT) for segmentation of intracranial structures due to its superior low contrast resolution. However, a reliable CT-based segmentation could improve patient management when MRI is not practical. Despite advancements in CT imaging, such as enhanced tissue differentiation using virtual monoenergetic imaging (VMI) from dual energy CT, volumetric analysis remains underexplored. Purpose: The aim was to evaluate the feasibility of using deep learning (DL) models for segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF)—using virtual monoenergetic images (VMI). Methods: The study included 26 patients... (More)

Background: Magnetic resonance imaging (MRI) has traditionally been preferred over computed tomography (CT) for segmentation of intracranial structures due to its superior low contrast resolution. However, a reliable CT-based segmentation could improve patient management when MRI is not practical. Despite advancements in CT imaging, such as enhanced tissue differentiation using virtual monoenergetic imaging (VMI) from dual energy CT, volumetric analysis remains underexplored. Purpose: The aim was to evaluate the feasibility of using deep learning (DL) models for segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF)—using virtual monoenergetic images (VMI). Methods: The study included 26 patients (training/validation: 21, test: 5) who underwent brain imaging on a dual-layer CT and a T1-weighted MR scan. MR-based segmentation of GM, WM, and CSF served as the ground truth for training and testing of the DL models. Models included a baseline U-Net++ trained on 70 keV VMIs and several U-Net and U-Net++ extensions designed to leverage spectral information from multiple VMIs (50, 70, and 120 keV). Model performance was evaluated using Dice Similarity Coefficient (DSC) and volumetric accuracy. Results: The U-Net++ (Aug) model, using VMIs as augmentations of the input data, outperformed the baseline and other models with DSC 0.84, 0.77, and 0.88 for WM, GM, and CSF, respectively. The superiority was significant compared to several of the other models, and most notably compared to the baseline model with DSC of 0.81 for WM (p = 0.002) and 0.75 for GM (p = 0.002). U-Net++ (Aug) had an average volumetric error of 12%, while U-Net (Gated) had the lowest error at 10%. Conclusions: This study demonstrates the feasibility of CT-based segmentation of intracranial tissue using DL and VMI. The improved accuracy of the U-Net++ (Aug) compared to the baseline model suggests that spectral information may enhance segmentation performance.

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author
; ; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Brain, Deep Learning, Tissue segmentation, X-Ray Computed Tomography Scanner
in
Medical Physics
volume
53
issue
1
article number
e70217
publisher
John Wiley & Sons Inc.
external identifiers
  • scopus:105025378296
  • pmid:41423435
ISSN
0094-2405
DOI
10.1002/mp.70217
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2025 The Author(s). Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.
id
c4c0bfd1-f0d8-43c7-94c0-476dae4babfb
date added to LUP
2026-03-06 15:28:55
date last changed
2026-05-30 04:23:28
@article{c4c0bfd1-f0d8-43c7-94c0-476dae4babfb,
  abstract     = {{<p>Background: Magnetic resonance imaging (MRI) has traditionally been preferred over computed tomography (CT) for segmentation of intracranial structures due to its superior low contrast resolution. However, a reliable CT-based segmentation could improve patient management when MRI is not practical. Despite advancements in CT imaging, such as enhanced tissue differentiation using virtual monoenergetic imaging (VMI) from dual energy CT, volumetric analysis remains underexplored. Purpose: The aim was to evaluate the feasibility of using deep learning (DL) models for segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF)—using virtual monoenergetic images (VMI). Methods: The study included 26 patients (training/validation: 21, test: 5) who underwent brain imaging on a dual-layer CT and a T1-weighted MR scan. MR-based segmentation of GM, WM, and CSF served as the ground truth for training and testing of the DL models. Models included a baseline U-Net++ trained on 70 keV VMIs and several U-Net and U-Net++ extensions designed to leverage spectral information from multiple VMIs (50, 70, and 120 keV). Model performance was evaluated using Dice Similarity Coefficient (DSC) and volumetric accuracy. Results: The U-Net++ (Aug) model, using VMIs as augmentations of the input data, outperformed the baseline and other models with DSC 0.84, 0.77, and 0.88 for WM, GM, and CSF, respectively. The superiority was significant compared to several of the other models, and most notably compared to the baseline model with DSC of 0.81 for WM (p = 0.002) and 0.75 for GM (p = 0.002). U-Net++ (Aug) had an average volumetric error of 12%, while U-Net (Gated) had the lowest error at 10%. Conclusions: This study demonstrates the feasibility of CT-based segmentation of intracranial tissue using DL and VMI. The improved accuracy of the U-Net++ (Aug) compared to the baseline model suggests that spectral information may enhance segmentation performance.</p>}},
  author       = {{Fransson, Veronica and Winzell, Filip and Ramgren, Birgitta and Christensen, Søren and Ydström, Kristina and Arvidsson, Ida and Overgaard, Niels Christian and Åström, Kalle and Heyden, Anders and Wassélius, Johan}},
  issn         = {{0094-2405}},
  keywords     = {{Brain; Deep Learning; Tissue segmentation; X-Ray Computed Tomography Scanner}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Medical Physics}},
  title        = {{Dual energy CT and deep learning for an automated volumetric segmentation of the major intracranial tissues : Feasibility and initial findings}},
  url          = {{http://dx.doi.org/10.1002/mp.70217}},
  doi          = {{10.1002/mp.70217}},
  volume       = {{53}},
  year         = {{2026}},
}