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. 2022 Oct;41(10):2856-2866.
doi: 10.1109/TMI.2022.3174513. Epub 2022 Sep 30.

Localization of Craniomaxillofacial Landmarks on CBCT Images Using 3D Mask R-CNN and Local Dependency Learning

Yankun Lang, Chunfeng Lian, Deqiang Xiao, Hannah Deng, Kim-Han Thung, Peng Yuan, Jaime Gateno, Tianshu Kuang, David M Alfi, Li Wang, Dinggang Shen, James J Xia, Pew-Thian Yap

Localization of Craniomaxillofacial Landmarks on CBCT Images Using 3D Mask R-CNN and Local Dependency Learning

Yankun Lang et al. IEEE Trans Med Imaging. 2022 Oct.

Abstract

Cephalometric analysis relies on accurate detection of craniomaxillofacial (CMF) landmarks from cone-beam computed tomography (CBCT) images. However, due to the complexity of CMF bony structures, it is difficult to localize landmarks efficiently and accurately. In this paper, we propose a deep learning framework to tackle this challenge by jointly digitalizing 105 CMF landmarks on CBCT images. By explicitly learning the local geometrical relationships between the landmarks, our approach extends Mask R-CNN for end-to-end prediction of landmark locations. Specifically, we first apply a detection network on a down-sampled 3D image to leverage global contextual information to predict the approximate locations of the landmarks. We subsequently leverage local information provided by higher-resolution image patches to refine the landmark locations. On patients with varying non-syndromic jaw deformities, our method achieves an average detection accuracy of 1.38± 0.95mm, outperforming a related state-of-the-art method.

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Figures

Fig. 1:
Fig. 1:
Our coarse-to-fine framework for CMF landmark localization in CBCT images.
Fig. 2:
Fig. 2:
Our workflow for CMF landmark detection, illustrated for detecting four landmarks in two anatomical regions. The Multi-RPN generates several location proposals with anatomical region labels (illustrated by two different colors), which are processed by NMS. Then, a recognition network detects each landmark from the proposals in the form of a regressed bounding box and a corresponding landmark label (illustrated by four different colors). Finally, NMS post-processing is applied to further filter out redundant bounding boxes.
Fig. 3:
Fig. 3:
Architecture of the proposed Mask R-CNN for detecting landmarks.
Fig. 4:
Fig. 4:
Predefined anatomical landmark regions with landmarks in the same anatomical region marked with the same colors.
Fig. 5:
Fig. 5:
Example landmark localization results.
Fig. 6:
Fig. 6:
Landmark localization for four patients with slight deformity (left), severe deformity (middle), and defects (right).
Fig. 7:
Fig. 7:
Detection errors for bounding boxes of different sizes.
See this image and copyright information in PMC

References

    1. Xia JJ, Gateno J, and Teichgraeber JF, “A new clinical protocol to evaluate cranio-maxillofacial deformity and to plan surgical correction,” J. Oral Maxillofacial Surg,, vol. 67, no. 10, pp. 2093–2106, 2009. - PMC - PubMed
    1. Dai J, Li Y, He K. and Sun J, “R-FCN: Object detection via region-based fully convolutional networks,” Proc. Adv. Neural Inf. Process. Syst, pp. 379–387, 2016.
    1. Tompson J, Goroshin R, Jain A, LeCun Y. and Bregler C, “Efficient object localization using convolutional networks,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit, pp. 648–656, 2015.
    1. Liang Z, Ding S, and Lin L, “Unconstrained faciallandmark localization with backbone-branches fully-convolutional networks,” [Online]. : https://arxiv.org/abs/1507.03409.
    1. Zhan Y, Dewan M, Harder M, Krishnan A, Zhou XS, “Robust automatic knee MR slice positioning through redundant and hierarchical anatomy detection,” IEEE Trans. Med. Imag, vol. 30, no. 12, pp. 2087–2100, Dec. 2011. - PubMed

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