Teeth And Root Canals Segmentation Using ZXYFormer With Uncertainty Guidance And Weight Transfer

TL;DR

This paper presents a novel coarse-to-fine transformer-based method with uncertainty guidance for simultaneous teeth and root canal segmentation in CBCT images, effectively handling large data, morphological differences, and weak edges.

Contribution

It introduces an inverse feature fusion transformer with uncertainty estimation and an auxiliary refinement branch for improved dental structure segmentation.

Findings

Outperforms existing methods on clinical CBCT data

Effectively segments weak edges and morphological differences

Demonstrates robustness on large high-resolution images

Abstract

This study attempts to segment teeth and root-canals simultaneously from CBCT images, but there are very challenging problems in this process. First, the clinical CBCT image data is very large (e.g., 672 *688 * 688), and the use of downsampling operation will lose useful information about teeth and root canals. Second, teeth and root canals are very different in morphology, and it is difficult for a simple network to identify them precisely. In addition, there are weak edges at the tooth, between tooth and root canal, which makes it very difficult to segment such weak edges. To this end, we propose a coarse-to-fine segmentation method based on inverse feature fusion transformer and uncertainty estimation to address above challenging problems. First, we use the downscaled volume data (e.g., 128 * 128 * 128) to conduct coarse segmentation and map it to the original volume to obtain the area of teeth and root canals. Then, we design a transformer with reverse feature fusion, which can bring better segmentation effect of different morphological objects by transferring deeper features to shallow features. Finally, we design an auxiliary branch to calculate and refine the difficult areas in order to improve the weak edge segmentation performance of teeth and root canals. Through the combined tooth and root canal segmentation experiment of 157 clinical high-resolution CBCT data, it is verified that the proposed method is superior to the existing tooth or root canal segmentation methods.