Background and Objectives

Measuring head-circumference (HC) length from ultrasound (US) images is a crucial clinical task to assess fetus growth. To lower intra- and inter-operator variability in HC length measuring, several computer-assisted solutions have been proposed in the years. Recently, a large number of deep-learning approaches is addressing the problem of HC delineation through the segmentation of the whole fetal head via convolutional neural networks (CNNs). Since the task is a edge-delineation problem, we propose a different strategy based on regression CNNs.

Methods

The proposed framework consists of a region-proposal CNN for head localization and centering, and a regression CNN for accurately delineate the HC. The first CNN is trained exploiting transfer learning, while we propose a training strategy for the regression CNN based on distance fields.

Results

The framework was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. A mean absolute difference of 1.90 ( ± 1.76) mm and a Dice similarity coefficient of 97.75 ( ± 1.32) % were achieved, overcoming approaches in the literature.

Conclusions

The experimental results showed the effectiveness of the proposed framework, proving its potential in supporting clinicians during the clinical practice.

中文翻译：

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美国胎儿图像的头围描述的回归框架

背景和目标

从超声（US）图像测量头围（HC）长度是评估胎儿生长的关键临床任务。为了降低HC长度测量中操作员之间和操作员之间的差异，近年来已提出了几种计算机辅助解决方案。最近，大量的深度学习方法通​​过卷积神经网络（CNN）分割整个胎儿的头部，解决了HC划定的问题。由于该任务是边缘描述问题，因此我们基于回归CNN提出了另一种策略。

方法

拟议的框架包括用于头部定位和居中的区域提案CNN，以及用于准确描绘HC的回归CNN。第一个CNN是利用转移学习进行训练的，而我们提出了一种基于距离场的回归CNN的训练策略。

结果

该框架在HC18 Challenge数据集上进行了测试，该数据集由999个训练图像和335个测试图像组成。克服了文献中的方法，获得了1.90（±1.76）mm的平均绝对差和97.75（±1.32）％的Dice相似系数。

结论

实验结果表明了该框架的有效性，证明了其在临床实践中支持临床医生的潜力。