Automatic Cephalometric Landmark Detection on X-ray Images Using a Deep-Learning Method
Source abstract
Abstract
Accurate automatic quantitative cephalometry are essential for orthodontics. However, manual labeling of cephalometric landmarks is tedious and subjective, which also must be performed by professional doctors. In recent years, deep learning has gained attention for its success in computer vision field. It has achieved large progress in resolving problems like image classification or image segmentation. In this paper, we propose a two-step method which can automatically detect cephalometric landmarks on skeletal X-ray images. First, we roughly extract a region of interest (ROI) patch for each landmark by registering the testing image to training images, which have annotated landmarks. Then, we utilize pre-trained networks with a backbone of ResNet50, which is a state-of-the-art convolutional neural network, to detect each landmark in each ROI patch. The network directly outputs the coordinates of the landmarks. We evaluate our method on two datasets: ISBI 2015 Grand Challenge in Dental X-ray Image Analysis and our own dataset provided by Shandong University. The experiments demonstrate that the proposed method can achieve satisfying results on both SDR (Successful Detection Rate) and SCR (Successful Classification Rate). However, the computational time issue remains to be improved in the future.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Dental Radiography and Imaging · Forensic Anthropology and Bioarchaeology Studies · Orthodontics and Dentofacial Orthopedics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate School of Information Science and Eng., Ritsumeikan University, Shiga 603-8577, Japan
Department of Biomedical Engineering, Shandong University, Shandong 250100, China
Funding
National Natural Science Foundation of China · Ministry of Education, Culture, Sports, Science and Technology
References (24)
Show source-provided references
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