测试不同扫描分辨率对牙齿分割精度的影响，找计算与准确的平衡点。
Evaluating the Suitability of Different Intraoral Scan Resolutions for Deep Learning-Based Tooth Segmentation
- 用PointMLP模型在2K到16K网格下训练，评估降采样影响
- 4K网格时分割精度下降不足3%，仍可满足临床需求
- 适合想部署轻量模型于牙科边缘设备的研究者和工程师
口内扫描广泛应用于牙科修复、治疗规划和正畸等数字牙科任务，包含丰富的拓扑信息，但手动标注耗时。深度学习方法可自动化牙齿分割，但典型扫描含超20万网格点，直接处理计算成本高。现有模型常在1万或1.6万网格点的降采样版本上训练。以往研究指出降采样可能降低分割精度，但具体影响尚不明确。本研究评估分辨率下降对性能的影响，使用PointMLP模型在16K、10K、8K、6K、4K和2K网格点的扫描上进行训练，再在高分辨率扫描上测试模型表现。目标是找到兼顾计算效率与分割精度的最佳分辨率。
原文摘要 · Abstract (English)
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.
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