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Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume

  • Trevor J. HuffEmail author
  • Parker E. Ludwig
  • David Salazar
  • Justin A. Cramer
Original Article
  • 83 Downloads

Abstract

Purpose

Hydrocephalus is a clinically significant condition which can have devastating consequences if left untreated. Currently available methods for quantifying this condition using CT imaging are unreliable and prone to error. The purpose of this study is to investigate the clinical utility of using convolutional neural networks to calculate ventricular volume and explore limitations.

Methods

A two-dimensional convolutional neural network was designed to perform fully automated ventricular segmentation on CT images. A total of 300 head CTs were collected and used in this exploration. Two hundred were used to train the network, 50 were used for validation, and 50 were used for testing.

Results

Dice scores for the left lateral, right lateral, and third ventricle segmentations were 0.92, 0.92, and 0.79, respectively; the coefficients of determination were r2 = 0.991, r2 = 0.994, and r2 = 0.976; the average volume differences between manual and automated segmentation were 0.821 ml, 0.587 ml, and 0.099 ml.

Conclusion

Two-dimensional convolutional neural network architectures can be used to accurately segment and quantify intracranial ventricle volume. While further refinements are necessary, it is likely these networks could be used as a clinical tool to quantify hydrocephalus accurately and efficiently.

Keywords

U-Net Convolutional neural network (CNN) Machine learning Intracranial ventricle volume Automated segmentation 

Notes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Compliance with ethical standards

Conflict of interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Ethical approval

For this type of study, formal consent is not required.

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Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Creighton University School of MedicineOmahaUSA
  2. 2.Department of BiomechanicsUniversity of Nebraska at OmahaOmahaUSA
  3. 3.University of Nebraska Medical CenterOmahaUSA

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