Merdietio Boedi Rizky, Banar Nikolay, De Tobel Jannick, Bertels Jeroen, Vandermeulen Dirk, Thevissen Patrick Werner
Department of Imaging and Pathology - Forensic Odontology, KU Leuven, Leuven, Belgium.
Department of Electrical Engineering - ESAT/PSI, KU Leuven, Leuven, Belgium.
J Forensic Sci. 2020 Mar;65(2):481-486. doi: 10.1111/1556-4029.14182. Epub 2019 Sep 5.
Staging third molar development is commonly used for age estimation in subadults. Automated developmental stage allocation to the mandibular left third molar in panoramic radiographs has been examined in a pilot study. This method used an AlexNet Deep Convolutional Neural Network (CNN) approach to stage lower left third molars, which had been selected by manually drawn bounding boxes around them. This method (bounding box AlexNet = BA) still contained parts of surrounding structures which may have affected the automated stage allocation performance. We hypothesize that segmenting only the third molar could further improve the automated stage allocation performance. Therefore, the current study aimed to determine and validate the effect of lower third molar segmentations on automated tooth development staging. Retrospectively, 400 panoramic radiographs were collected, processed and segmented in three ways: bounding box (BB), rough (RS), and full (FS) tooth segmentation. A DenseNet201 CNN was used for automated stage allocation. Automated staging results were compared with reference stages - allocated by human observers - overall and per stage. FS rendered the best results with a stage allocation accuracy of 0.61, a mean absolute difference of 0.53 stages and a Cohen's linear κ of 0.84. Misallocated stages were mostly neighboring stages, and DenseNet201 rendered better results than AlexNet by increasing the percentage of correctly allocated stages by 3% (BA compared to BB). FS increased the percentage of correctly allocated stages by 7% compared to BB. In conclusion, full tooth segmentation and a DenseNet CNN optimize automated dental stage allocation for age estimation.
第三磨牙发育分期常用于亚成年人的年龄估计。在一项初步研究中，对全景X线片中下颌左侧第三磨牙的自动发育阶段分配进行了研究。该方法使用AlexNet深度卷积神经网络（CNN）对左下第三磨牙进行分期，这些磨牙是通过手动绘制围绕它们的边界框来选择的。这种方法（边界框AlexNet = BA）仍然包含周围结构的部分，这可能会影响自动分期性能。我们假设仅分割第三磨牙可以进一步提高自动分期性能。因此，本研究旨在确定并验证下第三磨牙分割对自动牙齿发育分期的影响。回顾性地收集了400张全景X线片，以三种方式进行处理和分割：边界框（BB）、粗略（RS）和完整（FS）牙齿分割。使用DenseNet201 CNN进行自动分期。将自动分期结果与人类观察者分配的参考分期进行总体和各阶段的比较。FS的结果最佳，分期分配准确率为0.61，平均绝对差异为0.53个阶段，Cohen线性κ为0.84。错误分配的阶段大多是相邻阶段，并且DenseNet201通过将正确分配阶段的百分比提高3%（与BB相比的BA），比AlexNet取得了更好的结果。与BB相比，FS将正确分配阶段的百分比提高了7%。总之，完整牙齿分割和DenseNet CNN优化了用于年龄估计的自动牙齿分期分配。
