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The loss of cervical lordosis is a common degenerative disorder known to beassociated with abnormal spinal alignment. In recent years, ultrasound (US)imaging has been widely applied in the assessment of spine deformity and hasshown promising results. The objectives of this study are to automaticallysegment bony structures from the 3D US cervical spine image volume and toassess the cervical lordosis on the key sagittal frames. In this study, aportable ultrasound imaging system was applied to acquire cervical spine imagevolume. The nnU-Net was trained on to segment bony structures on the transverseimages and validated by 5-fold-cross-validation. The volume data werereconstructed from the segmented image series. An energy function indicatingintensity levels and integrity of bony structures was designed to extract theproxy key sagittal frames on both left and right sides for the cervical curvemeasurement. The mean absolute difference (MAD), standard deviation (SD) andcorrelation between the spine curvatures of the left and right sides werecalculated for quantitative evaluation of the proposed method. The DSC value ofthe nnU-Net model in segmenting ROI was 0.973. For the measurement of 22 laminacurve angles, the MAD, SD and correlation between the left and right sides ofthe cervical spine were 3.591, 3.432 degrees and 0.926, respectively. Theresults indicate that our method has a high accuracy and reliability in theautomatic segmentation of the cervical spine and shows the potential ofdiagnosing the loss of cervical lordosis using the 3D ultrasound imagingtechnique.

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