Summary of A Multi-stage Framework For 3d Individual Tooth Segmentation in Dental Cbct, by Chunshi Wang et al.
A Multi-Stage Framework for 3D Individual Tooth Segmentation in Dental CBCT
by Chunshi Wang, Bin Zhao, Shuxue Ding
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In a groundbreaking study, researchers have developed a multi-stage framework for accurate 3D tooth segmentation in dental cone beam computed tomography (CBCT). This innovative method achieves impressive results, securing third place in the “Semi-supervised Teeth Segmentation” 3D (STS-3D) challenge. The proposed approach addresses the challenges of limited annotated data and domain shift caused by different devices, making it a significant contribution to medical image processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way to segment 3D teeth in dental X-rays. This is important for diagnosing and treating tooth problems. They used special computer algorithms, called deep learning methods, which are usually very good at recognizing patterns in images. However, these methods need a lot of labeled data to learn from, which can take a long time to collect and label. The team also found that the same algorithms can struggle when trying to use them with different machines or devices. To solve this problem, they developed a multi-stage framework for 3D tooth segmentation in dental CBCT. |
Keywords
» Artificial intelligence » Deep learning » Semi supervised
