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International Journal of Computer Assisted Radiology and Surgery

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26-07-2019 | Original Article

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

Authors: Trevor J. Huff, Parker E. Ludwig, David Salazar, Justin A. Cramer

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 11/2019

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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.

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Title: Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume
Authors: Trevor J. Huff
Parker E. Ludwig
David Salazar
Justin A. Cramer
Publication date: 26-07-2019
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 11/2019
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-019-02038-5

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Abstract

Purpose

Methods

Results

Conclusion

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