基于多项式曲线拟合结合界标检测的齿根模糊边缘高分辨率分割
High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection
摘要：
口腔X线检查作为根管治疗诊断中最经济、最常规的辅助检查，已被口腔科医生广泛应用。传统的图像分割方法在分割边缘模糊的牙根时仍具有一定的挑战性。为此，我们提出了一种基于多项式曲线拟合与地标检测（HS-PCL）的高分辨率分割模型。该方法通过检测均匀分布在牙根边缘的多个特征点，拟合出一条光滑的多项式曲线作为牙根的分割，从而解决了边缘模糊的问题。在我们的模型中，最大数量的最短距离算法（MNSDA）提出了自动减少的负面影响的错误地标，被错误地检测和偏离牙根的拟合结果。数值实验结果表明，该方法不仅使Hausdorff 95（HD 95）和平均表面距离（ASD）分别减少了33.9%和42.1%，而且在微小数据量上也取得了良好的效果，大大提高了利用医学图像计算进行根管治疗效果自动评估的可行性。
英文摘要：
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.
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