High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection

TL;DR

This paper introduces HS-PCL, a high-resolution tooth root segmentation model using polynomial curve fitting and landmark detection, effectively handling fuzzy edges in dental X-ray images to improve accuracy and automation.

Contribution

The paper proposes a novel HS-PCL model with a landmark detection and polynomial fitting approach, including MNSDA for robustness against incorrect landmarks, advancing dental image segmentation.

Findings

Reduces Hausdorff95 by 33.9%

Decreases Average Surface Distance by 42.1%

Achieves high accuracy with limited datasets

Abstract

As the most economical and routine auxiliary examination in the diagnosis of root canal treatment, oral X-ray has been widely used by stomatologists. It is still challenging to segment the tooth root with a blurry boundary for the traditional image segmentation method. To this end, we propose a model for high-resolution segmentation based on polynomial curve fitting with landmark detection (HS-PCL). It is based on detecting multiple landmarks evenly distributed on the edge of the tooth root to fit a smooth polynomial curve as the segmentation of the tooth root, thereby solving the problem of fuzzy edge. In our model, a maximum number of the shortest distances algorithm (MNSDA) is proposed to automatically reduce the negative influence of the wrong landmarks which are detected incorrectly and deviate from the tooth root on the fitting result. Our numerical experiments demonstrate that the proposed approach not only reduces Hausdorff95 (HD95) by 33.9% and Average Surface Distance (ASD) by 42.1% compared with the state-of-the-art method, but it also achieves excellent results on the minute quantity of datasets, which greatly improves the feasibility of automatic root canal therapy evaluation by medical image computing.