Fully automatic cervical vertebrae segmentation via enhanced u²-net

Accurate segmentation of the cervical vertebrae in CT images can assist clinicians in analyzing the adolescent patient's growth and development and establishing an effective orthodontic plan. This work develops an enhanced U2-Net architecture for fully automatic cervical vertebrae segmentation in CT images. Specifically, such an enhanced architecture first creates a deepwise separable residual U-shape module (DUM) in different levels and a convolutional attention module embedded into a U-structure for encoding and decoding and obtains a coarse segmentation. Then, it reuses a DUM to refine the segmentation. We evaluated our method on 60 CT scans, with the experimental results showing that our method attains much better segmentation performance than state-of-the-art network models. Particularly, it can improve the dices similarity coefficient, intersection over union, precision, and recall from (0.9586, 0.9207, 0.9584, 0.9591) to (0.9755, 0.9524, 0.9832, 0.9708), respectively, while it can reduce the model parameters from 44.0M to 16.5M.