A DCNN system based on an iterative method for automatic landmark detection in cephalometric X-ray images. (July 2021)

• Record Type: Journal Article
• Authors: Wang, Lu; Ma, Lanfang; Li, Ying; Niu, Kai; He, Zhiqiang
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Cephalometric landmark detection -- Iterative method -- Deep convolutional neural network -- Image processing; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102757
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: An deep learning system is proposed for automatic landmark detection in cephalometric X-ray images. The proposed iterative method is used to fully extract and utilize the information associated with the landmarks in the images. The transfer learning method between models is introduced, which allows the models between various types of landmarks to achieve the effect of shared learning and achieve mutual supplementation of information. The system can effectively locate landmarks, providing considerable benefits in terms of reducing orthodontist workload and improving diagnostic accuracy and treatment efficiency. Abstract: Cephalometric analysis is a commonly used method in modern orthodontic clinical diagnosis and treatment. The traditional way of manually marking cephalometric landmarks takes a long time, and it is difficult to obtain stable detection accuracy because of the uneven professionalism of doctors. In order to efficiently and accurately locate the landmarks of X-ray cephalograms, we propose a deep convolutional neural network (DCNN) system for automatic landmark detection in cephalometric X-ray images. And the system incorporates an iterative method while introducing the ideas of transfer learning and data enhancement. We first preprocess and expand the dataset of the cephalometric X-ray images. Then we use the preprocessed image to train a deep convolutional neural network and obtain preliminary prediction areas for various landmarks. Next, for each type of landmark, a detection process based on the iterative method is used to gradually reduce the prediction range in five iterations, which achieves more precise landmark positioning. Finally, the system restores the final predicted position in the original image by reversing the predicted coordinates. Our training set and test set proposed in the 2015 IEEE International Symposium on Biomedical Imaging each contain 150 cephalometric X-ray images. The accuracy of the system predicting 19 pairs of landmarks in the image within the error range of two millimeters reaches 87.51% on average. The experimental results and comparisons show that the iterative method does improve the accuracy and stability of location prediction and our cephalometric image landmark detector is effective in locating landmarks.