Localization of vessel Region of Interest (ROI) from medical images provides an interactive approach that can assist doctors in evaluating carotid artery diseases. Accurate vessel detection is a prerequisite for the following procedures, like wall segmentation, plaque identification and 3D reconstruction. Deep learning models such as CNN have been widely used in medical image processing, and achieve state-of-the-art performance. Faster R-CNN is one of the most representative and successful methods for object detection. Using outputs of feature maps in different layers has been proved to be a useful way to improve the detection performance, however, the common method is to ensemble outputs of different layers directly, and the special characteristic and different importance of each layer haven’t been considered. In this work, we introduce a new network named Attention Layer R-CNN(AL R-CNN) and use it for automatic carotid artery detection, in which we integrate a new module named Attention Layer Part (ALP) into a basic Faster R-CNN system for better assembling feature maps of different layers. Experimental results on carotid dataset show that our method surpasses other state-of-the-art object detection systems.

中文翻译：

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使用基于注意层区域的卷积神经网络自动检测颈动脉

从医学图像中定位血管感兴趣区域 (ROI) 提供了一种交互式方法，可以帮助医生评估颈动脉疾病。准确的血管检测是以下程序的先决条件，如壁分割、斑块识别和 3D 重建。CNN 等深度学习模型已广泛应用于医学图像处理，并取得了最先进的性能。Faster R-CNN 是最具代表性和最成功的目标检测方法之一。使用不同层的特征图的输出已被证明是提高检测性能的有效方法，但是常用的方法是直接将不同层的输出集成在一起，而每一层的特殊特征和不同的重要性都没有得到体现。经过考虑的。在这项工作中，我们引入了一个名为 Attention Layer R-CNN(AL R-CNN) 的新网络，并将其用于自动检测颈动脉，其中我们将一个名为 Attention Layer Part (ALP) 的新模块集成到一个基本的 Faster R-CNN 系统中，以便更好地进行组装不同层的特征图。颈动脉数据集的实验结果表明，我们的方法优于其他最先进的对象检测系统。