Automated Lateral Ventricular and Cranial Vault Volume Measurements in 13,851 Patients Using Deep Learning Algorithms

Maragkos, Georgios A; Filippidis, Aristotelis S.; Chilamkurthy, Sasank; Salem, Mohamed M.; Tanamala, Swetha; Gomez-Paz, Santiago; Rao, Pooja; Moore, Justin M.; Papavassiliou, Efstathios; Hackney, David B.; Thomas, Ajith J.

doi:10.1016/j.wneu.2020.12.148

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…[6][7][8] Nonetheless, CT volumetry has been used for brain CT perfusion 9 , hemorrhage volume measurement 10 , and semi-quantitative volumetry to assess early brain ischemia 11 , and more recently for determination of the skull volume and the volume of the lateral ventricles. 12 With the recent advances of spectral CT, the tissue differentiation of brain CT can be increased, including the discrimination between normal grey and white matter as well as between healthy and diseased grey matter. 13 This is achieved using virtual monoenergetic images (VMI).…”

Section: Introductionmentioning

confidence: 99%

Deep learning volumetric brain segmentation based on spectral CT

Fransson¹,

Christensen²,

Ydström

et al. 2023

Medical Imaging 2023: Physics of Medical Imaging

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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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