论文标题
深度学习算法用于检测头部放射学图像的解剖参考点的效率
The efficiency of deep learning algorithms for detecting anatomical reference points on radiological images of the head profile
论文作者
论文摘要
在本文中，我们研究了深度学习算法在解决横向投影中使用完全卷积神经网络和完全卷积的神经网络在横向投影中检测到头部放射学图像的任务的效率。对每个选定的神经网络体系结构的检测解剖参考点的结果进行了比较，及其与正畸医生检测到解剖参考点时获得的结果的比较。根据获得的结果，得出的结论是，U-NET神经网络允许比完全卷积神经网络更准确地执行解剖参考点。 U-NET神经网络检测解剖参考点的结果更接近一组正畸医生检测参考点的平均结果。
In this article we investigate the efficiency of deep learning algorithms in solving the task of detecting anatomical reference points on radiological images of the head in lateral projection using a fully convolutional neural network and a fully convolutional neural network with an extended architecture for biomedical image segmentation - U-Net. A comparison is made for the results of detection anatomical reference points for each of the selected neural network architectures and their comparison with the results obtained when orthodontists detected anatomical reference points. Based on the obtained results, it was concluded that a U-Net neural network allows performing the detection of anatomical reference points more accurately than a fully convolutional neural network. The results of the detection of anatomical reference points by the U-Net neural network are closer to the average results of the detection of reference points by a group of orthodontists.
