论文标题
使用深卷积神经网络在超声图像序列中进行颈动脉壁分割
Carotid artery wall segmentation in ultrasound image sequences using a deep convolutional neural network
论文作者
论文摘要
这项研究的目的是在纵向超声图像上对颈动脉的内膜媒体复合物进行分割，以测量其厚度。我们提出了一种基于全自动区域的分割方法，涉及基于扩张的U-NET网络的基于监督区域的深度学习方法。它是在由两位专家注释的2176张图像组成的多中心数据库上使用5倍交叉验证进行训练和评估的。与参考注释相比，所得的平均绝对差（<120 um）小于观察者间的变异性（180 um）。成功率为98.7％，即只有1.3％需要手动校正的病例，已证明该方法是可靠的，因此可能建议在临床实践中使用。
The objective of this study is the segmentation of the intima-media complex of the common carotid artery, on longitudinal ultrasound images, to measure its thickness. We propose a fully automatic region-based segmentation method, involving a supervised region-based deep-learning approach based on a dilated U-net network. It was trained and evaluated using a 5-fold cross-validation on a multicenter database composed of 2176 images annotated by two experts. The resulting mean absolute difference (<120 um) compared to reference annotations was less than the inter-observer variability (180 um). With a 98.7% success rate, i.e., only 1.3% cases requiring manual correction, the proposed method has been shown to be robust and thus may be recommended for use in clinical practice.
