Abstract
BackgroundSegmentation of important structures in temporal bone CT is the basis of image-guided otologic surgery. Manual segmentation of temporal bone CT is time- consuming and laborious. We assessed the feasibility and generalization ability of a proposed deep learning model for automated segmentation of critical structures in temporal bone CT scans.MethodsThirty-nine temporal bone CT volumes including 58 ears were divided into normal (n = 20) and abnormal groups (n = 38). Ossicular chain disruption (n = 10), facial nerve covering vestibular window (n = 10), and Mondini dysplasia (n = 18) were included in abnormal group. All facial nerves, auditory ossicles, and labyrinths of the normal group were manually segmented. For the abnormal group, aberrant structures were manually segmented. Temporal bone CT data were imported into the network in unmarked form. The Dice coefficient (DC) and average symmetric surface distance (ASSD) were used to evaluate the accuracy of automatic segmentation.ResultsIn the normal group, the mean values of DC and ASSD were respectively 0.703, and 0.250 mm for the facial nerve; 0.910, and 0.081 mm for the labyrinth; and 0.855, and 0.107 mm for the ossicles. In the abnormal group, the mean values of DC and ASSD were respectively 0.506, and 1.049 mm for the malformed facial nerve; 0.775, and 0.298 mm for the deformed labyrinth; and 0.698, and 1.385 mm for the aberrant ossicles.ConclusionsThe proposed model has good generalization ability, which highlights the promise of this approach for otologist education, disease diagnosis, and preoperative planning for image-guided otology surgery.
Highlights
The anatomy of the temporal bone is highly complex
Few studies have focused on the segmentation of important structures using deep-learning networks in temporal bone computed tomography (CT) images. To address this gap in the field, we have proposed a deep-learning framework referred to as W-net architecture and performed preliminary training of the neural network to automatically segment critical structures in CT scans of normal adult conventional temporal bone [30]
Neural network learning is prone to overfitting, that is, the model can correctly recognize the data in the training set but has poor performance of recognizing data outside the training set
Summary
The anatomy of the temporal bone is highly complex It contains crucial structures including the facial nerve, cochlea, and ossicular chain, which are fine structures surrounded by a large amount of mastoid air cells [1]. Due to this complexity and large interpatient variation, In recent years, novel technologies using imageguided neurotologic surgery have been developed such. Some software tools that can enhance and accelerate the extraction of structures of interest have emerged, they still cannot achieve fully automatic segmentation [10] Considering these aspects, it is necessary to automatically and accurately identify significant structures in temporal bone CT images. We assessed the feasibility and generalization ability of a proposed deep learning model for automated segmentation of critical structures in temporal bone CT scans
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