  Three-dimensional (3D) ultrasound imaging technique has been applied for
scoliosis assessment, but current assessment method only uses coronal
projection image and cannot illustrate the 3D deformity and vertebra rotation.
The vertebra detection is essential to reveal 3D spine information, but the
detection task is challenging due to complex data and limited annotations. We
propose VertMatch, a two-step framework to detect vertebral structures in 3D
ultrasound volume by utilizing unlabeled data in semi-supervised manner. The
first step is to detect the possible positions of structures on transverse
slice globally, and then the local patches are cropped based on detected
positions. The second step is to distinguish whether the patches contain real
vertebral structures and screen the predicted positions from the first step.
VertMatch develops three novel components for semi-supervised learning: for
position detection in the first step, (1) anatomical prior is used to screen
pseudo labels generated from confidence threshold method; (2) multi-slice
consistency is used to utilize more unlabeled data by inputting multiple
adjacent slices; (3) for patch identification in the second step, the
categories are rebalanced in each batch to solve imbalance problem.
Experimental results demonstrate that VertMatch can detect vertebra accurately
in ultrasound volume and outperforms state-of-the-art methods. VertMatch is
also validated in clinical application on forty ultrasound scans, and it can be
a promising approach for 3D assessment of scoliosis.
