Deep learning-based approach for the automatic segmentation of adult and pediatric temporal bone computed tomography images

Abstract

Background: Automatic segmentation of temporal bone computed tomography (CT) images is fundamental to image-guided otologic surgery and the intelligent analysis of CT images in the field of otology. This study was conducted to test a convolutional neural network (CNN) model that can automatically segment almost all temporal bone anatomy structures in adult and pediatric CT images. Methods: A dataset comprising 80 annotated CT volumes was collected, of which 40 samples were obtained from adults and 40 from children. A further 60 annotated CT volumes (30 from adults and 30 from children) were used to train the model. The remaining 20 annotated CT volumes were employed to determine the model’s generalizability for automatic segmentation. Finally, the Dice coefficient (DC) and average symmetric surface distance (ASSD) were utilized as metrics to evaluate the performance of the CNN model. Two independent-sample t-tests were used to compare the test set results of adults and children. Results: In the adult test set, the mean DC values of all the structures ranged from 0.714 to 0.912, and the ASSD values were less than 0.24 mm for 11 structures. In the pediatric test set, the mean DC values of all the structures ranged from 0.658 to 0.915, and the ASSD values were less than 0.18 mm for 11 structures. There was no statistically significant difference between the adult and child test sets in most temporal bone structures. Conclusions: Our CNN model shows excellent automatic segmentation performance and good generalizability for both adult and pediatric temporal bone CT images, which can help to advance otologist education, intelligent imaging diagnosis, surgery simulation, application of augmented reality, and preoperative planning for image-guided otology surgery.