Cerebellum measurements of routinely acquired ultrasound (US) images are commonly used to estimate gestational age and to assess structural abnormalities of the developing central nervous system. Investigating associations between the developing cerebellum and neurodevelopmental outcomes post partum requires standardized cerebellum measurements from large clinical datasets. Such investigations have the potential to identify structural changes that can be used as biomarkers to predict growth and neurodevelopmental outcomes. For this purpose, high throughput, accurate, and unbiased measurements are necessary to replace existing manual, semi-automatic, and automated approaches which are tedious and lack reproducibility and accuracy. In this study, we propose a new deep learning algorithm for automated segmentation of the fetal cerebellum from 2-dimensional (2D) US images. We propose ResU-Net-c a semantic segmentation model optimized for fetal cerebellum structure. We leverage U-Net as a base model with the integration of residual blocks (Res) and introduce dilation convolution in the last two layers to segment the cerebellum (c) from noisy US images. Our experiments used a 5-fold cross-validation with 588 images for training and 146 for testing. Our ResU-Net-c achieved a mean Dice Score Coefficient, Hausdorff Distance, Recall, and Precision of 87.00%, 28.15, 86.00%, and 90.00%, respectively. The superiority of the proposed method over the other U-Net based methods is statistically significant (p <; 0.001). Our proposed method can be leveraged to enable high throughput image analysis in clinical research fetal US images and can be employed in the biometric assessment in fetal US images on a larger scale.

中文翻译：

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使用卷积神经网络对二维胎儿超声脑图像中的小脑进行语义分割


常规采集的超声 (US) 图像的小脑测量通常用于估计胎龄和评估发育中的中枢神经系统的结构异常。研究小脑发育与产后神经发育结果之间的关联需要来自大型临床数据集的标准化小脑测量。此类研究有可能确定结构变化，这些变化可用作预测生长和神经发育结果的生物标志物。为此，需要高通量、准确且无偏差的测量来取代现有的手动、半自动和自动化方法，这些方法繁琐且缺乏可重复性和准确性。在这项研究中，我们提出了一种新的深度学习算法，用于从二维 (2D) US 图像中自动分割胎儿小脑。我们提出 ResU-Net-c 一种针对胎儿小脑结构优化的语义分割模型。我们利用 U-Net 作为基础模型，集成残差块 (Res)，并在最后两层引入扩张卷积，以从嘈杂的 US 图像中分割小脑 (c)。我们的实验使用 5 倍交叉验证，其中 588 个图像用于训练，146 个图像用于测试。我们的 ResU-Net-c 的平均 Dice 得分系数、Hausdorff 距离、召回率和精度分别为 87.00%、28.15、86.00% 和 90.00%。所提出的方法相对于其他基于 U-Net 的方法的优越性具有统计显着性 (p <; 0.001)。我们提出的方法可用于在临床研究胎儿超声图像中实现高通量图像分析，并且可用于更大规模的胎儿超声图像的生物特征评估。