Automated Freehand 3D Ultrasound Reconstruction of the Midpalatal Suture using Deep Learning

Hoang T.H.; Quinones D.J.A.; Wang J.; Nguyen K.-C.T.; Major P.; Ta D.; Lou Edmond H.; Le Lawrence H. · Conference Proceedings - ICCE 2026: 2026 IEEE 11th International Conference on Communications and Electronics (2026) · DOI: 10.1109/icce70609.2026.11658397

Resumen

Ultrasound (US) imaging is emerging as a promising radiation-free modality for three-dimensional (3D) visualization of the midpalatal suture (MPS), a key anatomical structure in rapid maxillary expansion treatment. However, current approaches are largely confined to laboratory settings, rely on mechanical scanning systems, and require extensive manual image processing. Additionally, interpreting and processing of intraoral US images remain challenging and timeconsuming for clinicians. To overcome these challenges and facilitate clinical translation, this study proposes a fully automated, end-to-end framework for freehand 3D US visualization of the MPS. The framework integrates optical tracking-based freehand US acquisition, automated deep-learning-based anatomical segmentation, and 3D image reconstruction within a single workflow. Feasibility was evaluated using ex-vivo pig jaw samples with simulated MPS opening. Quantitative validation against gold-standard microcomputed tomography demonstrated strong agreement for MPS separation measurements (R2=0. 9 2), with a mean absolute difference of 0. 2 2 ± 0. 1 6 ∼ m m and 97% of measurements within the clinically acceptable tolerance of 0.5 mm. These findings demonstrated the feasibility of automated freehand 3D US for objective, repeatable, and radiation-free assessment of MPS opening.

Instituciones: Universidad Nacional de Ingeniería