The efficiency of deep learning algorithms for detecting anatomical reference points on radiological images of the head profile
This work addresses a domain-specific problem for orthodontics, offering an incremental improvement in automating reference point detection.
The study tackled the problem of detecting anatomical reference points on radiological head images by comparing a fully convolutional neural network and U-Net, finding that U-Net achieved more accurate detection results closer to those of orthodontists.
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.