• Ryan T Kellogg, Min S Park, M Harrison Snyder, Alexandria Marino, Sohil Patel, Xue Feng, and Jan Vargas.
• Department of Neurological Surgery, University of Virginia, Charlottesville, Virginia, USA. Electronic address: rtk4u@virginia.edu.
• World Neurosurg. 2023 Jul 1; 175: e976e983e976-e983.

BackgroundThe diagnosis of hydrocephalus is dependent on clinical symptoms and radiographic findings including ventriculomegaly. Our goal was to generate a data set of ventricular volume utilizing non-pathologic computed tomography (CT) scans for adults to help define reference ventricle size.MethodsWe performed a retrospective analysis of non-contrast head CTs for adults at a single institution to identify patients who had undergone imaging and did not have a diagnosis of hydrocephalus, history of ventriculoperitoneal shunting, or treatments for hydrocephalus. A convolutional neural network was trained on hand-segmented scans from a variety of age ranges and then utilized to automate the segmentation of the entire data set.ResultsVentricles on 866 CT scans were segmented to generate a reference range of volumes for both male and female individuals ranging in age from 18-99 years. The generated data were binned by age ranges.ConclusionsWe have developed a convolutional neural network that can segment the ventricles on CT scans of adult patients over a range of ages. This network was used to measure the ventricular volume of non-pathologic head CTs to produce reference ranges for several age bins. This data set could be utilized to aid in the diagnosis of hydrocephalus by comparing potentially pathologic scans to reference ventricular volumes.Copyright © 2023 Elsevier Inc. All rights reserved.

25 keywords...

hide…