Summary of Teeth-seg: An Efficient Instance Segmentation Framework For Orthodontic Treatment Based on Anthropic Prior Knowledge, by Bo Zou et al.
Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledge
by Bo Zou, Shaofeng Wang, Hao Liu, Gaoyue Sun, Yajie Wang, FeiFei Zuo, Chengbin Quan, Youjian Zhao
First submitted to arxiv on: 1 Apr 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 The proposed TeethSEG framework is designed to tackle teeth localization, segmentation, and labeling in 2D images for modern dentistry. The framework consists of stacked Multi-Scale Aggregation (MSA) blocks and an Anthropic Prior Knowledge (APK) layer, which enables efficient boundary establishment while emphasizing semantic particulars. This is particularly important given the subtle shape differences between some teeth, position and shape variations across subjects, and presence of abnormalities in the dentition. The TeethSEG framework outperforms state-of-the-art segmentation models on dental image segmentation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Teeth localization, segmentation, and labeling are crucial for modern dentistry to improve diagnostics, treatment planning, and population-based oral health studies. However, general instance segmentation frameworks struggle with subtle tooth shape differences, position variations, and abnormalities like caries and edentulism. To address these challenges, researchers developed a new framework called TeethSEG. It combines stacked Multi-Scale Aggregation (MSA) blocks and an Anthropic Prior Knowledge (APK) layer to efficiently establish clear segmentation boundaries while emphasizing semantic details. The team also created the first open-sourced intraoral image dataset, IO150K, which features over 150k annotated photos taken by orthodontists using a human-machine hybrid algorithm. |
Keywords
» Artificial intelligence » Image segmentation » Instance segmentation
