Segmentation of 3D Dental Images Using Deep Learning

TL;DR

This paper presents a multi-phase deep learning system for accurate 3D dental image segmentation, combining data reduction, CNN classification, and KNN-based correction, validated on clinical data.

Contribution

It introduces a hybrid deep learning framework with data compression and post-processing for improved 3D dental image segmentation.

Findings

High segmentation accuracy on clinical dental images

Robustness demonstrated across diverse cases

Efficient processing due to data decimation

Abstract

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.