Deeply Learned Cervical Vertebrae Maturation Staging in CT Images

Fan Zhang, Linya Zheng, Chen Lin, Liping Huang, Yuming Bai, Yinran Chen, Xiongbiao Luo

Abstract: Cervical vertebrae age estimation empowers clinicians to determine the development status of children and adolescents for precise orthodontic diagnosis and treatment. This work proposes a fully automated cervical vertebrae maturation staging framework that uses deeply learned CT image segmentation and classification. Specifically, such a two-step framework first employs convolutional neural networks (i.e., nnU-Net) to precisely segment the cervical vertebrae bones. Then, we propose parallel regression-classification networks for the bone staging using the segmented results. Specifically, the regression path introduces prior knowledge (i.e., bone anatomical parameters) to supervise the classification path. We evaluate our method on two clinical CT databases (60 balance volumes and 85 imbalance volumes), with the experimental results showing that our method attains higher classification accuracy than state-of-the-art methods. In particular, it can improve the accuracy from (0.5833, 0.5294) to (0.6667, 0.6471) on (balance, imbalance) data, respectively. Besides, our method also achieves an average accuracy of (0.65, 0.6353) for the 5-fold cross-validation.