Multimodal Contrastive Pretraining of CBCT and IOS for Enhanced Tooth Segmentation

• URL: http://arxiv.org/abs/2509.07923v1
• Date: Tue, 09 Sep 2025 17:05:04 GMT
• Title: Multimodal Contrastive Pretraining of CBCT and IOS for Enhanced Tooth Segmentation
• Authors: Moo Hyun Son, Juyoung Bae, Zelin Qiu, Jiale Peng, Kai Xin Li, Yifan Lin, Hao Chen,
• Abstract summary: We present ToothMCL, a Tooth Multimodal Contrastive Learning for pretraining that integrates volumetric (CBCT) and surface-based (IOS) modalities.<n>Our approach effectively models fine-grained anatomical features, enabling precise multi-class segmentation and accurate identification of F'ederation Dentaire Internationale (FDI) tooth numbering.
• Score: 8.574756499299374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital dentistry represents a transformative shift in modern dental practice. The foundational step in this transformation is the accurate digital representation of the patient's dentition, which is obtained from segmented Cone-Beam Computed Tomography (CBCT) and Intraoral Scans (IOS). Despite the growing interest in digital dental technologies, existing segmentation methodologies frequently lack rigorous validation and demonstrate limited performance and clinical applicability. To the best of our knowledge, this is the first work to introduce a multimodal pretraining framework for tooth segmentation. We present ToothMCL, a Tooth Multimodal Contrastive Learning for pretraining that integrates volumetric (CBCT) and surface-based (IOS) modalities. By capturing modality-invariant representations through multimodal contrastive learning, our approach effectively models fine-grained anatomical features, enabling precise multi-class segmentation and accurate identification of F\'ed\'eration Dentaire Internationale (FDI) tooth numbering. Along with the framework, we curated CBCT-IOS3.8K, the largest paired CBCT and IOS dataset to date, comprising 3,867 patients. We then evaluated ToothMCL on a comprehensive collection of independent datasets, representing the largest and most diverse evaluation to date. Our method achieves state-of-the-art performance in both internal and external testing, with an increase of 12\% for CBCT segmentation and 8\% for IOS segmentation in the Dice Similarity Coefficient (DSC). Furthermore, ToothMCL consistently surpasses existing approaches in tooth groups and demonstrates robust generalizability across varying imaging conditions and clinical scenarios.

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