Teeth Localization and Lesion Segmentation in CBCT Images Using SpatialConfiguration-Net and U-Net

Arnela Hadzic, Barbara Kirnbauer, Darko Štern, Martin Urschler

2024

Abstract

The localization of teeth and segmentation of periapical lesions in cone-beam computed tomography (CBCT) images are crucial tasks for clinical diagnosis and treatment planning, which are often time-consuming and require a high level of expertise. However, automating these tasks is challenging due to variations in shape, size, and orientation of lesions, as well as similar topologies among teeth. Moreover, the small volumes occupied by lesions in CBCT images pose a class imbalance problem that needs to be addressed. In this study, we propose a deep learning-based method utilizing two convolutional neural networks: the SpatialConfiguration-Net (SCN) and a modified version of the U-Net. The SCN accurately predicts the coordinates of all teeth present in an image, enabling precise cropping of teeth volumes that are then fed into the U-Net which detects lesions via segmentation. To address class imbalance, we compare the performance of three reweighting loss functions. After evaluation on 144 CBCT images, our method achieves a 97.3% accuracy for teeth localization, along with a promising sensitivity and specificity of 0.97 and 0.88, respectively, for subsequent lesion detection.

Paper Citation

in Harvard Style

Hadzic A., Kirnbauer B., Štern D. and Urschler M. (2024). Teeth Localization and Lesion Segmentation in CBCT Images Using SpatialConfiguration-Net and U-Net. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 290-297. DOI: 10.5220/0012305200003660

in Bibtex Style

@conference{visapp24,
author={Arnela Hadzic and Barbara Kirnbauer and Darko Štern and Martin Urschler},
title={Teeth Localization and Lesion Segmentation in CBCT Images Using SpatialConfiguration-Net and U-Net},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={290-297},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012305200003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Teeth Localization and Lesion Segmentation in CBCT Images Using SpatialConfiguration-Net and U-Net
SN - 978-989-758-679-8
AU - Hadzic A.
AU - Kirnbauer B.
AU - Štern D.
AU - Urschler M.
PY - 2024
SP - 290
EP - 297
DO - 10.5220/0012305200003660
PB - SciTePress