Niu Li, Zhong Shengwei, Yang Zhiyu, Tan Baochun, Zhao Junjie, Zhou Wei, Zhang Peng, Hua Lingchen, Sun Weibin, Li Houxuan
Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province 210008, China.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu Province 210094, China.
Dentomaxillofac Radiol. 2024 Feb 8;53(2):127-136. doi: 10.1093/dmfr/twad012.
Instance-level tooth segmentation extracts abundant localization and shape information from panoramic radiographs (PRs). The aim of this study was to evaluate the performance of a mask refinement network that extracts precise tooth edges.
A public dataset which consists of 543 PRs and 16211 labelled teeth was utilized. The structure of a typical Mask Region-based Convolutional Neural Network (Mask RCNN) was used as the baseline. A novel loss function was designed focus on producing accurate mask edges. In addition to our proposed method, 3 existing tooth segmentation methods were also implemented on the dataset for comparative analysis. The average precisions (APs), mean intersection over union (mIoU), and mean Hausdorff distance (mHAU) were exploited to evaluate the performance of the network.
A novel mask refinement region-based convolutional neural network was designed based on Mask RCNN architecture to extract refined masks for individual tooth on PRs. A total of 3311 teeth were correctly detected from 3382 tested teeth in 111 PRs. The AP, precision, and recall were 0.686, 0.979, and 0.952, respectively. Moreover, the mIoU and mHAU achieved 0.941 and 9.7, respectively, which are significantly better than the other existing segmentation methods.
This study proposed an efficient deep learning algorithm for accurately extracting the mask of any individual tooth from PRs. Precise tooth masks can provide valuable reference for clinical diagnosis and treatment. This algorithm is a fundamental basis for further automated processing applications.
实例级牙齿分割可从全景X光片（PR）中提取丰富的定位和形状信息。本研究的目的是评估一种能提取精确牙齿边缘的掩码细化网络的性能。
使用了一个包含543张PR和16211颗标注牙齿的公共数据集。以典型的基于掩码区域的卷积神经网络（Mask RCNN）结构作为基线。设计了一种新颖的损失函数，专注于生成准确的掩码边缘。除了我们提出的方法外，还在该数据集上实现了3种现有的牙齿分割方法进行对比分析。利用平均精度（AP）、平均交并比（mIoU）和平均豪斯多夫距离（mHAU）来评估网络性能。
基于Mask RCNN架构设计了一种新颖的基于掩码细化区域的卷积神经网络，用于从PR中提取单个牙齿的细化掩码。在111张PR中的3382颗测试牙齿中，共正确检测出3311颗牙齿。AP、精度和召回率分别为0.686、0.979和0.952。此外，mIoU和mHAU分别达到0.941和9.7，明显优于其他现有的分割方法。
本研究提出了一种高效的深度学习算法，用于从PR中准确提取任何单个牙齿的掩码。精确的牙齿掩码可为临床诊断和治疗提供有价值的参考。该算法是进一步自动化处理应用的基础。
