Assessing CT-based volumetric analysis via deep learning for idiopathic normal pressure hydrocephalus

2026-08-05

Meera Srikrishna

Woosung Seo

Anna Zettergren

Silke Kern

Daniel Cantré

Florian Gessler

Houman Sotoudeh

Jakob Seidlitz

Joshua D. Bernstock

Lars‐Olof Wahlund

Eric Westman

Ingmar Skoog

Johan Virhammar

David Fällmar

Michael Schöll *

DOI：10.1093/braincomms/fcag300

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Brain computed tomography (CT) is an accessible and commonly utilized technique for assessing brain structure. In cases of idiopathic normal pressure hydrocephalus (iNPH), the presence of ventriculomegaly is often neuroradiologically evaluated by visual rating and manual measurement of each image. Previously, we have developed a deep-learning-model that utilizes transfer learning from magnetic resonance imaging (MRI) for CT-based intracranial tissue segmentation. Accordingly, herein we aimed to enhance the segmentation of ventricular cerebrospinal fluid (VCSF) in brain CT scans and assess the performance of automated brain CT volumetrics in iNPH patient diagnostics.

Assessing CT-based volumetric analysis via deep learning for idiopathic normal pressure hydrocephalus

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