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Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N. and Liang, J. (2018) Unet++: A Nested U-Net Architecture for Medical Image Segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Vol. 4, 3-11.

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• TITLE: Single Tooth Segmentation on Panoramic X-Rays Using End-to-End Deep Neural Networks

  AUTHORS: Yu Sun, Jing Feng, Huang Du, Juan Liu, Baochuan Pang, Cheng Li, Jinxian Li, Dehua Cao

  KEYWORDS: Single Tooth Segmentation, Teeth Counting, Panoramic X-Ray, Combinatorial Loss

  JOURNAL NAME: Open Journal of Stomatology, Vol.14 No.6, June 19, 2024

  ABSTRACT: In dentistry, panoramic X-ray images are extensively used by dentists for tooth structure analysis and disease diagnosis. However, the manual analysis of these images is time-consuming and prone to misdiagnosis or overlooked. While deep learning techniques have been employed to segment teeth in panoramic X-ray images, accurate segmentation of individual teeth remains an underexplored area. In this study, we propose an end-to-end deep learning method that effectively addresses this challenge by employing an improved combinatorial loss function to separate the boundaries of adjacent teeth, enabling precise segmentation of individual teeth in panoramic X-ray images. We validate the feasibility of our approach using a challenging dataset. By training our segmentation network on 115 panoramic X-ray images, we achieve an intersection over union (IoU) of 86.56% for tooth segmentation and an accuracy of 65.52% in tooth counting on 87 test set images. Experimental results demonstrate the significant improvement of our proposed method in single tooth segmentation compared to existing methods.