Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network.

The use of two cascaded U-Net allows high-resolution predictions for small regions of interest in the imaging data. The proposed method is fast and allows a user-independent image segmentation, producing objective and repeatable results that can be used in automated surgical planning procedures.