  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.
