Deep learning volumetric brain segmentation based on spectral CT
(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.
(Less)
- author
- Fransson, V. LU ; Christensen, S. ; Ydström, K. LU and Wassélius, J. LU
- organization
- publishing date
- 2023
- 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}}, }