  Automatic segmentation of vertebral bodies (VBs) and intervertebral discs
(IVDs) in 3D magnetic resonance (MR) images is vital in diagnosing and treating
spinal diseases. However, segmenting the VBs and IVDs simultaneously is not
trivial. Moreover, problems exist, including blurry segmentation caused by
anisotropy resolution, high computational cost, inter-class similarity and
intra-class variability, and data imbalances. We proposed a two-stage
algorithm, named semi-supervised hybrid spine network (SSHSNet), to address
these problems by achieving accurate simultaneous VB and IVD segmentation. In
the first stage, we constructed a 2D semi-supervised DeepLabv3+ by using cross
pseudo supervision to obtain intra-slice features and coarse segmentation. In
the second stage, a 3D full-resolution patch-based DeepLabv3+ was built. This
model can be used to extract inter-slice information and combine the coarse
segmentation and intra-slice features provided from the first stage. Moreover,
a cross tri-attention module was applied to compensate for the loss of
inter-slice and intra-slice information separately generated from 2D and 3D
networks, thereby improving feature representation ability and achieving
satisfactory segmentation results. The proposed SSHSNet was validated on a
publicly available spine MR image dataset, and remarkable segmentation
performance was achieved. Moreover, results show that the proposed method has
great potential in dealing with the data imbalance problem. Based on previous
reports, few studies have incorporated a semi-supervised learning strategy with
a cross attention mechanism for spine segmentation. Therefore, the proposed
method may provide a useful tool for spine segmentation and aid clinically in
spinal disease diagnoses and treatments. Codes are publicly available at:
https://github.com/Meiyan88/SSHSNet.
