Creighton University School of Medicine, Omaha, NE, USA.
Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, USA.
Int J Comput Assist Radiol Surg. 2019 Nov;14(11):1923-1932. doi: 10.1007/s11548-019-02038-5. Epub 2019 Jul 26.
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.
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.
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 r = 0.991, r = 0.994, and r = 0.976; the average volume differences between manual and automated segmentation were 0.821 ml, 0.587 ml, and 0.099 ml.
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.
脑积水是一种临床意义重大的疾病，如果不及时治疗，可能会产生严重的后果。目前使用 CT 成像定量评估这种疾病的方法不可靠且容易出错。本研究旨在探讨使用卷积神经网络计算脑室容积的临床应用价值，并探讨其局限性。
设计了一个二维卷积神经网络，以对 CT 图像进行全自动脑室分割。共采集了 300 个头 CT 用于这项探索研究。其中 200 个头 CT 用于训练网络，50 个头 CT 用于验证，50 个头 CT 用于测试。
左侧、右侧和第三脑室的分割 Dice 评分分别为 0.92、0.92 和 0.79；决定系数 r 分别为 0.991、0.994 和 0.976；手动和自动分割之间的平均体积差异分别为 0.821ml、0.587ml 和 0.099ml。
二维卷积神经网络架构可用于准确分割和量化颅内脑室容积。虽然还需要进一步改进，但这些网络可能被用作临床工具，以准确高效地量化脑积水。
