Ultrasound image segmentation is a challenging task due to a low signal‐to‐noise ratio and poor image quality. Although several approaches based on the convolutional neural network (CNN) have been applied to ultrasound image segmentation, they have weak generalization ability. We propose an end‐to‐end, multiple‐channel and atrous CNN designed to extract a greater amount of semantic information for segmentation of ultrasound images.

中文翻译：

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用于超声图像分割的多通道无声卷积网络

由于低信噪比和较差的图像质量，超声图像分割是一项具有挑战性的任务。尽管基于卷积神经网络（CNN）的几种方法已应用于超声图像分割，但它们具有较弱的泛化能力。我们提出了一种端到端，多通道和无规则的CNN，旨在提取大量语义信息以用于超声图像的分割。