# A Critical Analysis of the Limitation of Deep Learning based 3D Dental Mesh Segmentation Methods in Segmenting Partial Scans

**Authors:** Ananya Jana, Aniruddha Maiti, Dimitris N. Metaxas  
**arXiv:** [2305.00244](https://arxiv.org/abs/2305.00244)  
**Date:** 2023-04-29  
**Summary page:** https://www.summarizepaper.com/en/paper/2305.00244v1/

## Key points

- Tooth segmentation from intraoral scans is crucial in digital dentistry.
- Deep learning-based algorithms have been developed for tooth segmentation, achieving high accuracy.
- Existing techniques assume a full jaw model and report accuracy based on full jaw scans.
- However, in certain cases, a full jaw tooth scan may not be necessary or available, causing practical issues.
- The authors applied deep learning-based tooth segmentation techniques to partial intraoral scans to understand their robustness.
- Results showed that existing methods underperform significantly when dealing with partial scans.
- This highlights the need for developing robust tooth segmentation techniques that do not rely on strong assumptions about the full jaw model.
- The study titled "A Critical Analysis of the Limitation of Deep Learning based 3D Dental Mesh Segmentation Methods in Segmenting Partial Scans" provides valuable insights into the limitations of current tooth segmentation techniques.
- Experiments conducted by the authors using various deep learning-based segmentation methods on partial intraoral scans showed drastically reduced performance compared to full jaw models.
- This research sheds light on an important practical issue in digital dentistry and emphasizes the need for more robust approaches that can handle partial scans without relying on complete jaw models.
- The findings contribute to our understanding of the limitations faced by current deep learning-based tooth segmentation techniques and will guide future research efforts towards developing more accurate and reliable methods for handling partial intraoral scans.

## Summary

Tooth segmentation from intraoral scans plays a crucial role in digital dentistry. Many deep learning-based algorithms have been developed for this task, achieving high accuracy. However, most of these techniques make an implicit assumption of a full jaw model and report accuracy based on full jaw scans. In certain cases, a full jaw tooth scan may not be necessary or available, which raises practical issues. To understand the robustness of currently available deep learning-based tooth segmentation techniques and address this problem, the authors applied these techniques to partial intraoral scans. The results revealed that the existing methods underperform significantly when dealing with partial scans. This analysis and comparison highlight the severity of the issue and emphasize the need for developing robust tooth segmentation techniques that do not rely on strong assumptions about the full jaw model. The study titled "A Critical Analysis of the Limitation of Deep Learning based 3D Dental Mesh Segmentation Methods in Segmenting Partial Scans" by Ananya Jana, Aniruddha Maiti, and Dimitris N. Metaxas provides valuable insights into the limitations of current tooth segmentation techniques. The authors conducted experiments using various deep learning-based segmentation methods on partial intraoral scans and found that these methods had drastically reduced performance compared to their performance on full jaw models. This research is significant as it sheds light on an important practical issue in digital dentistry. By demonstrating the underperformance of existing tooth segmentation techniques on partial scans, it highlights the need for more robust approaches that can handle such scenarios without relying on complete jaw models. The findings presented in this work contribute to our understanding of the limitations faced by current widely used deep learning-based tooth segmentation techniques. This knowledge will guide future research efforts towards developing more accurate and reliable tooth segmentation methods that can effectively handle partial intraoral scans. Overall, this study serves as a critical analysis of the limitations associated with deep learning-based 3D dental mesh segmentation methods when applied to segmenting partial scans. The insights gained from this analysis will pave the way for the development of more robust tooth segmentation techniques that can overcome the challenges posed by partial intraoral scans, ultimately improving the accuracy and effectiveness of digital dentistry.