Evaluating the Suitability of Different Intraoral Scan Resolutions for Deep Learning-Based Tooth Segmentation

TL;DR

This study assesses how reducing the resolution of intraoral scans affects the accuracy of deep learning-based tooth segmentation, aiming to find an optimal balance between computational efficiency and performance.

Contribution

It systematically evaluates the impact of various scan resolutions on segmentation accuracy using a deep learning model, providing insights for practical deployment.

Findings

Lower resolutions degrade segmentation accuracy.

16K mesh resolution offers a good balance.

Performance drops significantly below 8K mesh cells.

Abstract

Intraoral scans are widely used in digital dentistry for tasks such as dental restoration, treatment planning, and orthodontic procedures. These scans contain detailed topological information, but manual annotation of these scans remains a time-consuming task. Deep learning-based methods have been developed to automate tasks such as tooth segmentation. A typical intraoral scan contains over 200,000 mesh cells, making direct processing computationally expensive. Models are often trained on downsampled versions, typically with 10,000 or 16,000 cells. Previous studies suggest that downsampling may degrade segmentation accuracy, but the extent of this degradation remains unclear. Understanding the extent of degradation is crucial for deploying ML models on edge devices. This study evaluates the extent of performance degradation with decreasing resolution. We train a deep learning model (PointMLP) on intraoral scans decimated to 16K, 10K, 8K, 6K, 4K, and 2K mesh cells. Models trained at lower resolutions are tested on high-resolution scans to assess performance. Our goal is to identify a resolution that balances computational efficiency and segmentation accuracy.