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 ave

SUBMITTER: Ke J 

PROVIDER: S-EPMC10006112 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature