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Deep learning volumetric brain segmentation based on spectral CT

Fransson, V. LU orcid ; Christensen, S. ; Ydström, K. LU and Wassélius, J. LU (2023) Medical Imaging 2023: Physics of Medical Imaging In Progress in Biomedical Optics and Imaging - Proceedings of SPIE 12463.
Abstract

The purpose of this pilot study was to evaluate if a deep learning network can be used for brain segmentation of grey and white matter using spectral computed tomography (CT) images. Spectral CT has the advantage of a lower noise level and an increased soft tissue contrast, compared to conventional CT, which should make it better suited for segmentation tasks. Being able to do volumetric assessments on CT, not only magnetic resonance imaging (MRI) would be of great clinical benefit. The training set consisted of two patients and the validation data set of one patient. Included patients had a brain CT from a spectral CT as well as a T1-weighted MRI. MRI was used for an MR-based segmentation using FreeSurfer. A convolutional neural... (More)

The purpose of this pilot study was to evaluate if a deep learning network can be used for brain segmentation of grey and white matter using spectral computed tomography (CT) images. Spectral CT has the advantage of a lower noise level and an increased soft tissue contrast, compared to conventional CT, which should make it better suited for segmentation tasks. Being able to do volumetric assessments on CT, not only magnetic resonance imaging (MRI) would be of great clinical benefit. The training set consisted of two patients and the validation data set of one patient. Included patients had a brain CT from a spectral CT as well as a T1-weighted MRI. MRI was used for an MR-based segmentation using FreeSurfer. A convolutional neural network was trained to identify grey and white matter in virtual monoenergetic images (70 keV) from spectral CT, using the MR-based segmentation as reference, and tested to assess its' performance. The network was able to identify both grey and white matter in roughly the correct areas. In general, there was an overestimation of grey matter. These results motivate further studies, as we predict that the network will be more accurate when trained on a larger data set.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
brain segmentation, deep learning, spectral CT, virtual monoenergetic imaging, volumetric
host publication
Medical Imaging 2023 : Physics of Medical Imaging - Physics of Medical Imaging
series title
Progress in Biomedical Optics and Imaging - Proceedings of SPIE
editor
Yu, Lifeng ; Fahrig, Rebecca and Sabol, John M.
volume
12463
article number
1246336
publisher
SPIE
conference name
Medical Imaging 2023: Physics of Medical Imaging
conference location
San Diego, United States
conference dates
2023-02-19 - 2023-02-23
external identifiers
  • scopus:85160804972
ISSN
1605-7422
ISBN
9781510660311
DOI
10.1117/12.2654161
language
English
LU publication?
yes
id
9fd08081-270c-4732-8243-8d9338427ce9
date added to LUP
2023-08-24 14:50:38
date last changed
2023-08-24 14:50:38
@inproceedings{9fd08081-270c-4732-8243-8d9338427ce9,
  abstract     = {{<p>The purpose of this pilot study was to evaluate if a deep learning network can be used for brain segmentation of grey and white matter using spectral computed tomography (CT) images. Spectral CT has the advantage of a lower noise level and an increased soft tissue contrast, compared to conventional CT, which should make it better suited for segmentation tasks. Being able to do volumetric assessments on CT, not only magnetic resonance imaging (MRI) would be of great clinical benefit. The training set consisted of two patients and the validation data set of one patient. Included patients had a brain CT from a spectral CT as well as a T1-weighted MRI. MRI was used for an MR-based segmentation using FreeSurfer. A convolutional neural network was trained to identify grey and white matter in virtual monoenergetic images (70 keV) from spectral CT, using the MR-based segmentation as reference, and tested to assess its' performance. The network was able to identify both grey and white matter in roughly the correct areas. In general, there was an overestimation of grey matter. These results motivate further studies, as we predict that the network will be more accurate when trained on a larger data set.</p>}},
  author       = {{Fransson, V. and Christensen, S. and Ydström, K. and Wassélius, J.}},
  booktitle    = {{Medical Imaging 2023 : Physics of Medical Imaging}},
  editor       = {{Yu, Lifeng and Fahrig, Rebecca and Sabol, John M.}},
  isbn         = {{9781510660311}},
  issn         = {{1605-7422}},
  keywords     = {{brain segmentation; deep learning; spectral CT; virtual monoenergetic imaging; volumetric}},
  language     = {{eng}},
  publisher    = {{SPIE}},
  series       = {{Progress in Biomedical Optics and Imaging - Proceedings of SPIE}},
  title        = {{Deep learning volumetric brain segmentation based on spectral CT}},
  url          = {{http://dx.doi.org/10.1117/12.2654161}},
  doi          = {{10.1117/12.2654161}},
  volume       = {{12463}},
  year         = {{2023}},
}