Age estimation based on 3D pulp chamber segmentation of first molars from cone-beam-computed tomography by integrated deep learning and level set.

Abstract

To develop an automatic segmentation method to segment the pulp chamber of first molars from 3D cone-beam-computed tomography (CBCT) images, and to estimate ages by calculated pulp volumes. Patients with CBCT scans were retrospectively identified. The age estimation was formulated as CBCT image segmentation using a coarse-to-fine strategy by integrated deep learning (DL) and level set (LS), followed by establishing a linear regression model. On the training data, DL model was trained for coarse segmentation. The validation set was to determine the optimal DL model, and a LS method established on it was to refine the coarse segmentation. On the testing data, the integrated DL and LS method was applied for pulp chamber segmentation, followed by volume calculation and age estimation. Statistical analysis was performed by Wilcoxon rank sum test to demonstrate gender difference in pulp chamber volume, and volume difference between maxillary and mandibular molars. Wilcoxon signed-rank test was adopted to compare true and estimated ages. A total of 180 CBCT studies were randomly divided into 37/10/133 patients for training, validation, and testing data, respectively. In the training and validation sets, the results showed high spatial overlaps between manual and automatic segmentation (dice = 87.8%). For the testing set, the estimated human ages were not significantly different with true human age (p= 0.57), with a correlation coefficient r= 0.74. An integrated DL and LS method was able to segment pulp chamber of first molars from 3D CBCT images, and the derived pulp chamber volumes could effectively estimate the human ages.