Supervised And Semi Supervised Multi Structure Segmentation And Landmark Detection In Dental Data

Supervised and Semi-supervised Multi-structure Segmentation and Landmark Detection in Dental Data

Supervised and Semi-supervised Multi-structure Segmentation and Landmark Detection in Dental Data

This book constitutes three challenges that were held in conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 6, 2024: ToothFairy challenge(ToothFairy2: Multi-Structure Segmentation in CBCT Volumes), Semi-supervised Teeth Segmentation (STS 2024), and the 3DTeethLand (3D Teeth Landmarks Detection Challenge). The 21 papers presented in this volume were carefully reviewed and selected from 28 submissions. ToothFairy challenges focused on the development of deep learning frameworks to segment anatomical structures in CBCTs by incrementally extending the amount of publicly available 3D-annotated CBCT scans and providing the first publicly available fully annotated datasets. The STS Challenge promoted the development of teeth segmentation in panoramic X-ray images and CBCT scans. It also provided instance annotations for different teeth, including pertinent category information. The 3DTeethLand24 Challenge played a key role in advancing automation and leveraging AI to optimize orthodontic treatments. It also aims to tackle the challenge of limited access to data, providing a valuable resource that encourages community engagement in this vital area with potential clinical implications.

Semi-supervised Tooth Segmentation

This book constitutes the proceedings of the First MICCAI 2023 Challenge on Semi-supervised Tooth Segmentation, SemiToothSeg 2023, held in Conjunction with MICCAI 2023, in Vancouver, BC, Canada, on October 8, 2023. The 16 full papers presented in this book were carefully reviewed and selected from 64 submissions. The papers were written by participants in the STS challenge to describe their solutions for automatic teeth segmentation using the offcial training dataset released for this purpose. In general, this challenge aims to promote the development of the teeth segmentation in panoramic X-ray images and dental CBCT scans.