STS 2024 : 2nd Semi-supervised Teeth Segmentation MICCAI Challenge

🤗 Introduction

The 2D panoramic X-ray image and 3D dental cone-beam computed tomography (CBCT) examination are the efficient ways for dentists to determine invisible caries, impacted teeth and supernumerary teeth among children. Yet, identifying teeth from panoramic X-ray images or CBCT scans and further manually annotating the teeth is time-consuming and labor-intensive. Thus, we usually cannot obtain a huge number of labeled cases, which limits the development of deep learning algorithms for segmenting teeth and automatically analyzing diseases. As a potential alternative, semi-supervised learning can explore useful information from unlabeled cases.

In this year, we aim to promote the development of the tooth segmentation. We extend previous 🤓STS 2023 Challenge from binary semantic settings to multi-instance and multi-class segmentation settings, with an emphasis on instance-level segmentation. Specifically, we host two challenge tracks, and are proud to announce the launch of the challenge workshop. If you are interested in publishing a workshop paper, please 👉read the CfP and submit your paper :

Track 1: 2D Panoramic X-ray Segmentation

The segmentation algorithm is expected to accurately segment 32 permanent teeth (including wisdom teeth) and 20 possible deciduous teeth in panoramic X-ray images.

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Track 2: 3D Dental CBCT Segmentation

The segmentation algorithm needs to accurately segment 32 permanent teeth (including wisdom teeth) in 3D dental CBCT volume scans of different imaging fields.

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