Segmentation of 3D Dental Images Using Deep Learning
This work addresses the problem of accurate 3D dental image segmentation for medical analysis, though it appears incremental as it combines existing techniques without claiming major breakthroughs.
The paper tackles 3D dental image segmentation by proposing a multi-phase deep learning system that hybridizes methods including decimation compression, CNN segmentation, and KNN-based transformation, achieving segmentation into fifteen classes with demonstrated precision and robustness on a private clinical benchmark.
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.