Automatic Segmentation of Temporal Bone Structures from Clinical Conventional CT Using a CNN Approach

xlomafota13 shared this article with you from Inoreader Automatic Segmentation of Temporal Bone Structures from Clinical Conventional CT Using a CNN Approach Via The International Journal of Medical Robotics and Computer Assisted Surgery 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. This article is protected by copyright. All rights reserved. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.