Processing and Segmentation of Human Teeth from 2D Images using Weakly Supervised Learning

• URL: http://arxiv.org/abs/2311.07398v2
• Date: Mon, 26 Feb 2024 09:54:34 GMT
• Title: Processing and Segmentation of Human Teeth from 2D Images using Weakly Supervised Learning
• Authors: Tom\'a\v{s} Kunzo, Viktor Kocur, Luk\'a\v{s} Gajdo\v{s}ech, Martin Madaras
• Abstract summary: We propose a weakly supervised approach for teeth segmentation that reduces the need for manual annotation. Our method utilizes the output heatmaps and intermediate feature maps from a keypoint detection network to guide the segmentation process. Our method offers a cost-effective and efficient solution for teeth segmentation in real-world dental applications.
• Score: 1.6385815610837167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Teeth segmentation is an essential task in dental image analysis for accurate diagnosis and treatment planning. While supervised deep learning methods can be utilized for teeth segmentation, they often require extensive manual annotation of segmentation masks, which is time-consuming and costly. In this research, we propose a weakly supervised approach for teeth segmentation that reduces the need for manual annotation. Our method utilizes the output heatmaps and intermediate feature maps from a keypoint detection network to guide the segmentation process. We introduce the TriDental dataset, consisting of 3000 oral cavity images annotated with teeth keypoints, to train a teeth keypoint detection network. We combine feature maps from different layers of the keypoint detection network, enabling accurate teeth segmentation without explicit segmentation annotations. The detected keypoints are also used for further refinement of the segmentation masks. Experimental results on the TriDental dataset demonstrate the superiority of our approach in terms of accuracy and robustness compared to state-of-the-art segmentation methods. Our method offers a cost-effective and efficient solution for teeth segmentation in real-world dental applications, eliminating the need for extensive manual annotation efforts.

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