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7 April 2023 Deep learning volumetric brain segmentation based on spectral CT
V. Fransson, S. Christensen, K. Ydström, J. Wassélius
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Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 1246336 (2023) https://doi.org/10.1117/12.2654161
Event: SPIE Medical Imaging, 2023, San Diego, California, United States
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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 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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V. Fransson, S. Christensen, K. Ydström, and J. Wassélius "Deep learning volumetric brain segmentation based on spectral CT", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 1246336 (7 April 2023); https://doi.org/10.1117/12.2654161
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KEYWORDS
Image segmentation
Brain
Computed tomography
X-ray computed tomography
Deep learning
Neuroimaging
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