MICCAI STSR 2025 Challenge: Semi-Supervised Teeth and Pulp Segmentation and CBCT-IOS Registration
PositiveArtificial Intelligence
- The MICCAI STSR 2025 Challenge has been announced, focusing on semi-supervised learning for teeth and pulp segmentation using Cone-Beam Computed Tomography (CBCT) and Intraoral Scanning (IOS). The challenge includes two main tasks: segmenting teeth and pulp canals in CBCT images and registering CBCT with IOS data. Participants will have access to a dataset comprising 60 labeled and 640 unlabeled IOS samples, alongside 30 labeled and 250 unlabeled CBCT scans.
- This initiative is significant as it addresses the critical issue of data scarcity in digital dentistry, which hampers the development of automated solutions for pulp canal segmentation and cross-modal registration. By fostering community participation and encouraging the submission of open-source solutions, the challenge aims to enhance the capabilities of semi-supervised learning in this specialized field.
- The challenge reflects a growing trend in the application of deep learning techniques in medical imaging, particularly in the realm of dental diagnostics. Innovations such as nnU-Net and Mamba-like State Space Models are being utilized to improve segmentation accuracy, while frameworks like nnActive are emerging to evaluate active learning in 3D biomedical segmentation. This convergence of technology and healthcare highlights the ongoing efforts to refine automated processes and improve patient outcomes in digital dentistry.
— via World Pulse Now AI Editorial System
