  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.
