Multimodal Contrastive Pretraining of CBCT and IOS for Enhanced Tooth Segmentation

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


MPhil Thesis Defence


Title: "Multimodal Contrastive Pretraining of CBCT and IOS for Enhanced Tooth
Segmentation"

By

Mr. Moo Hyun SON


Abstract:

Digital dentistry represents a transformative shift in modern dental practice. 
The foundation of this transformation lies in the accurate digital 
representation of a patient's dentition, obtained from segmented Cone-Beam 
Computed Tomography (CBCT) and Intraoral Scans (IOS). Despite increasing 
interest in digital dental technologies, existing segmentation methods often 
lack rigorous validation and demonstrate limited clinical applicability. This 
work presents ToothMCL, the first multimodal pretraining framework for dental 
segmentation, addressing a critical and previously unmet challenge. Unlike 
prior single-modality approaches, ToothMCL integrates volumetric (CBCT) and 
surface-based (IOS) imaging through contrastive learning to capture 
modality-invariant anatomical representations. Using a curated dataset of 
3,867 paired CBCT—IOS samples, ToothMCL achieves state-of-the-art 
performance, improving Dice scores by 12% for CBCT and 8% for IOS across the 
largest and most diverse evaluation to date. Our findings highlight the 
transformative potential of large-scale multimodal pretraining to advance 
clinical workflows in digital dentistry.


Date:                   Monday, 12 January 2026

Time:                   2:00pm-4:00pm

Venue:                  Room 3494
                        Lifts 25/26

Chairman:               Dr. Long CHEN

Committee Members:      Dr. Hao CHEN (Supervisor)
                        Dr. Dan XU