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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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MICCAI STS 2024 challenge: Semi-supervised instance-level tooth segmentation in panoramic X-ray and CBCT images.

Yaqi Wang1, Zhi Li2, Chengyu Wu3

  • 1Innovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University, Hangzhou, China.

Medical Image Analysis
|February 25, 2026
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Summary

Semi-supervised learning (SSL) significantly improves automated tooth segmentation in dental imaging (Orthopantomograms and Cone-Beam Computed Tomography) by overcoming data scarcity. The STS 2024 Challenge showcased advanced SSL methods outperforming fully-supervised baselines.

Keywords:
CBCTOPGSemi-supervised learningTooth segmentation

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Area of Science:

  • Medical Image Analysis
  • Dental Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Automated tooth segmentation in dental imaging (Orthopantomograms - OPGs, Cone-Beam Computed Tomography - CBCT) is crucial for diagnostics.
  • Manual instance-level annotation for creating large datasets is labor-intensive, leading to data scarcity.
  • Semi-supervised learning (SSL) presents a promising solution to address data limitations in medical image segmentation.

Purpose of the Study:

  • To benchmark and advance semi-supervised learning (SSL) methods for instance-level tooth segmentation in dental imaging.
  • To organize the 2nd Semi-supervised Teeth Segmentation (STS 2024) Challenge to foster research and development in this area.
  • To provide a large-scale, publicly available dataset for OPG and CBCT images with instance-level annotations.

Main Methods:

  • Organized the STS 2024 Challenge, providing a dataset of over 90,000 2D OPG and 3D CBCT images with annotations.
  • Evaluated valid, open-source, deep learning-based SSL submissions from participating teams.
  • Utilized hybrid semi-supervised frameworks combining foundational models (e.g., SAM) with multi-stage refinement pipelines.

Main Results:

  • The STS 2024 Challenge attracted 114 (OPG) and 106 (CBCT) registered teams.
  • Winning SSL models significantly outperformed a fully-supervised nnU-Net baseline.
  • Top methods achieved substantial improvements: >44 percentage points in Instance Affinity (IA) for OPGs and 61 percentage points in Instance Dice for CBCT.

Conclusions:

  • Semi-supervised learning is highly effective for complex, instance-level medical image segmentation tasks with limited labeled data.
  • Hybrid SSL approaches leveraging foundational models and multi-stage refinement pipelines show the most promise.
  • The challenge dataset and submitted code are publicly available on GitHub, promoting transparency and reproducibility in dental AI research.