Deep learning-based registration of two-dimensional dental images with edge specific loss

Maria-Pavlina Kalla; Theodoros P. Vagenas; Theodore L. Economopoulos; George K. Matsopoulos

doi:10.1117/1.JMI.10.3.034002

1 June 2023 Deep learning-based registration of two-dimensional dental images with edge specific loss

Maria-Pavlina Kalla, Theodoros P. Vagenas, Theodore L. Economopoulos, George K. Matsopoulos

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 3, 034002 (June 2023). https://doi.org/10.1117/1.JMI.10.3.034002

Abstract

Purpose

Image registration is a very common procedure in dental applications for aligning images. Registration between pairs of images taken from different angles can improve diagnosis. Our study presents an edge-enhanced unsupervised deep learning (DL)-based deformable registration framework for aligning two-dimensional (2D) pairs of dental x-ray images.

Approach

The proposed neural network is based on the combination of a U-Net like structure, which produces a displacement field, combined with spatial transformer networks, which produce the transformed image. The proposed structure is trained end-to-end by minimizing a weighted loss function consisting of three parts corresponding to image similarity, edge similarity, and registration restrictions. In this regard, the proposed edge specific loss enhances the unsupervised training of the registration framework without the need of supervision through anatomical structures.

Results

The proposed framework was applied to two datasets, a set of 104 x-ray images of mandibles, arranged in 2600 pairs for training and testing and a set of 17 pairs of pre- and post-operative reconstructed panoramic images. The proposed model outperformed both conventional registration methods and DL-based techniques for both qualitative and quantitative assessment, in most of the compared metrics concerning intensity similarity and edge distances.

Conclusions

The proposed framework achieved accurate and fast deformable alignment of pairs of 2D dental radiographic images. The edge-based module of the loss function enhances the unsupervised learning by directing the network toward deformations that take into consideration the edges of the depicted objects (teeth, bone, and tissue), which are crucial in diagnosis.

Citation Download Citation

Maria-Pavlina Kalla, Theodoros P. Vagenas, Theodore L. Economopoulos, and George K. Matsopoulos "Deep learning-based registration of two-dimensional dental images with edge specific loss," Journal of Medical Imaging 10(3), 034002 (1 June 2023). https://doi.org/10.1117/1.JMI.10.3.034002

Received: 27 September 2022; Accepted: 15 May 2023; Published: 1 June 2023

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KEYWORDS

Image registration

Education and training

Neural networks

Deformation

Panoramic photography

Tunable filters

Radiography

Purpose

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