Automatic bony structure segmentation and curvature estimation on ultrasound cervical spine images -- a feasibility study

The loss of cervical lordosis is a common degenerative disorder known to be associated with abnormal spinal alignment. In recent years, ultrasound (US) imaging has been widely applied in the assessment of spine deformity and has shown promising results. The objectives of this study are to automatically segment bony structures from the 3D US cervical spine image volume and to assess the cervical lordosis on the key sagittal frames. In this study, a portable ultrasound imaging system was applied to acquire cervical spine image volume. The nnU-Net was trained on to segment bony structures on the transverse images and validated by 5-fold-cross-validation. The volume data were reconstructed from the segmented image series. An energy function indicating intensity levels and integrity of bony structures was designed to extract the proxy key sagittal frames on both left and right sides for the cervical curve measurement. The mean absolute difference (MAD), standard deviation (SD) and correlation between the spine curvatures of the left and right sides were calculated for quantitative evaluation of the proposed method. The DSC value of the nnU-Net model in segmenting ROI was 0.973. For the measurement of 22 lamina curve angles, the MAD, SD and correlation between the left and right sides of the cervical spine were 3.591, 3.432 degrees and 0.926, respectively. The results indicate that our method has a high accuracy and reliability in the automatic segmentation of the cervical spine and shows the potential of diagnosing the loss of cervical lordosis using the 3D ultrasound imaging technique.