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An Efficient Deep Learning Based Coarse-to-Fine Cephalometric Landmark Detection Method

Yu SONG, Xu QIAO, Yutaro IWAMOTO, Yen-Wei CHEN, Yili CHEN

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Summary :

Accurate and automatic quantitative cephalometry analysis is of great importance in orthodontics. The fundamental step for cephalometry analysis is to annotate anatomic-interested landmarks on X-ray images. Computer-aided automatic method remains to be an open topic nowadays. In this paper, we propose an efficient deep learning-based coarse-to-fine approach to realize accurate landmark detection. In the coarse detection step, we train a deep learning-based deformable transformation model by using training samples. We register test images to the reference image (one training image) using the trained model to predict coarse landmarks' locations on test images. Thus, regions of interest (ROIs) which include landmarks can be located. In the fine detection step, we utilize trained deep convolutional neural networks (CNNs), to detect landmarks in ROI patches. For each landmark, there is one corresponding neural network, which directly does regression to the landmark's coordinates. The fine step can be considered as a refinement or fine-tuning step based on the coarse detection step. We validated the proposed method on public dataset from 2015 International Symposium on Biomedical Imaging (ISBI) grand challenge. Compared with the state-of-the-art method, we not only achieved the comparable detection accuracy (the mean radial error is about 1.0-1.6mm), but also largely shortened the computation time (4 seconds per image).

Publication
IEICE TRANSACTIONS on Information Vol.E104-D No.8 pp.1359-1366
Publication Date
2021/08/01
Publicized
2021/05/14
Online ISSN
1745-1361
DOI
10.1587/transinf.2021EDP7001
Type of Manuscript
PAPER
Category
Image Processing and Video Processing

Authors

Yu SONG
  Ritsumeikan University
Xu QIAO
  Shandong University
Yutaro IWAMOTO
  Ritsumeikan University
Yen-Wei CHEN
  Ritsumeikan University
Yili CHEN
  Zhejiang University

Keyword