论文标题
使用深度学习对3D牙科图像进行分割
Segmentation of 3D Dental Images Using Deep Learning
论文作者
论文摘要
3D图像分割是许多医学分析和识别方案的最新至关重要的一步。实际上，由于其重要性和影响力，它代表了一个相关的研究主题和基本挑战。本文提供了一个基于深度学习的系统，该系统杂交了各种有效的方法，以获取最佳的3D分割输出。首先，为了减少数据量并加速处理时间，建议并证明了脱节压缩技术的应用。然后，我们使用CNN模型将牙科图像分为15个分开的类。最后，用于去除孤立的网格和校正牙齿形式的目的进行特殊的基于KNN的转换。实验证明了在私人临床基准内应用于3D牙科图像的精度和鲁棒性。
3D image segmentation is a recent and crucial step in many medical analysis and recognition schemes. In fact, it represents a relevant research subject and a fundamental challenge due to its importance and influence. This paper provides a multi-phase Deep Learning-based system that hybridizes various efficient methods in order to get the best 3D segmentation output. First, to reduce the amount of data and accelerate the processing time, the application of Decimate compression technique is suggested and justified. We then use a CNN model to segment dental images into fifteen separated classes. In the end, a special KNN-based transformation is applied for the purpose of removing isolated meshes and of correcting dental forms. Experimentations demonstrate the precision and the robustness of the selected framework applied to 3D dental images within a private clinical benchmark.
