Abstract
Background
Automatic segmentation of temporal bone structures from patients' conventional CT data plays an important role in the image‐guided cochlear implant surgery. Existing convolutional neural network approaches have difficulties in segmenting such small tubular structures.
Methods
We propose a light‐weight 3D CNN referred to as W‐Net to achieve multi‐objective segmentation of temporal bone structures including the cochlear labyrinth, ossicular chain and facial nerve from conventional temporal bone CT images. Data augmentation with morphological enhancement is proposed to increase the segmentation accuracy of small tubular structures. Evaluation against the state‐of‐the‐art methods is performed.
Results
Our method achieved mean Dice similarity coefficients (DSCs) of 0.90, 0.85 and 0.77 for the cochlear labyrinth, ossicular chain and facial nerve, respectively. These results were also close to the DSCs between human expert annotators (0.91, 0.91, 0.72).
Conclusions
Our method achieves human‐level accuracy in the segmentation of the cochlear labyrinth, ossicular chain and facial nerve.
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