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. 2023 Jul:175:e976-e983.
doi: 10.1016/j.wneu.2023.04.055. Epub 2023 Apr 21.

Establishment of Age- and Sex-Specific Reference Cerebral Ventricle Volumes

Ryan T Kellogg  1 , Min S Park  2 , M Harrison Snyder  3 , Alexandria Marino  2 , Sohil Patel  4 , Xue Feng  5 , Jan Vargas  6
Affiliations

Establishment of Age- and Sex-Specific Reference Cerebral Ventricle Volumes

Ryan T Kellogg et al. World Neurosurg. 2023 Jul.

Abstract

Background: The 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.

Methods: We 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.

Results: Ventricles 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.

Conclusions: We 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.

Keywords: Artificial intelligence; Deep learning; Hydrocephalus; Segmentation; Ventricle; Ventriculomegaly.

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Conflict of interest statement

Conflict of interest statement: The authors declare that the article content was composed in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1.
Figure 1.
Example of the ventricular system segmented by the CNN represented as a 3D model.
Figure 2.
Figure 2.
(A) Average ventricular volume for female patients grouped by age with 5%, 10%, 25%, 50%, 75%, 90%, and 95% curves. (B) Average ventricular volume for male patients grouped by age with 5%, 10%, 25%, 50%, 75%, 90%, and 95% curves. (C) Average percentage of ventricular volume based on total intracranial volume for female patients grouped by age with 5%, 10%, 25%, 50%, 75%, 90%, and 95% curves. (D) Average percentage of ventricular volume based on total intracranial volume for male patients grouped by age with 5%, 10%, 25%, 50%, 75%, 90%, and 95% curves.
Figure 3.
Figure 3.
Results with outliers removed. (A) Average ventricular volume for female patients grouped by age with 5%, 10%, 25%, 50%, 75%, 90%, and 95% curves. (B) Average ventricular volume for male patients grouped by age with 5%, 10%, 25%, 50%, 75%, 90%, and 95% curves. (C) Average percentage of ventricular volume based on total intracranial volume for female patients grouped by age with 5%, 10%, 25%, 50%, 75%, 90%, and 95% curves. (D) Average percentage of ventricular volume based on total intracranial volume for male patients grouped by age with 5%, 10%, 25%, 50%, 75%, 90%, and 95% curves.
See this image and copyright information in PMC

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