Cochlear CT image segmentation based on u-net neural network

Abstract

ObjectiveBefore cochlear implantation, accurately identifying the cochlea's morphology is necessary. This study proposes an improved network model based on U-Net, which can realize automatic segmentation of human cochlear anatomy in computed tomography (CT) images. MethodsThe CT scan data of 100 patients requiring cochlear implantation diagnosed in our hospital were randomly collected. It was divided into a training set (n = 75) and a test set (n = 25). All data were manually segmented by two clinicians. At the same time, U-Net was used for deep learning of the above data. The cochlea in the test set was compared with the dice similarity coefficient (DSC) and 95% Hausdorff surface distance (HD95%) by manual and automatic segmentation. ResultsThe DSC and HD95% of manual cochlear image segmentation were 0.761 and 4.343, respectively. The DSC and HD95% were 0.742 and 4.217, respectively, for automatic segmentation of cochlear structure using the U-Net network structure. The difference of DSC and HD95% between the two segmentation methods was not statistically significant (P > 0.05). ConclusionsThe cochlea can be thoroughly segmented automatically based on the U-Net neural network, and the precision is close to manual segmentation.