  Deep convolutional neural networks are widely used in medical image
segmentation but require many labeled images for training. Annotating
three-dimensional medical images is a time-consuming and costly process. To
overcome this limitation, we propose a novel semi-supervised segmentation
method that leverages mostly unlabeled images and a small set of labeled images
in training. Our approach involves assessing prediction uncertainty to identify
reliable predictions on unlabeled voxels from the teacher model. These voxels
serve as pseudo-labels for training the student model. In voxels where the
teacher model produces unreliable predictions, pseudo-labeling is carried out
based on voxel-wise embedding correspondence using reference voxels from
labeled images. We applied this method to automate hip bone segmentation in CT
images, achieving notable results with just 4 CT scans. The proposed approach
yielded a Hausdorff distance with 95th percentile (HD95) of 3.30 and IoU of
0.929, surpassing existing methods achieving HD95 (4.07) and IoU (0.927) at
their best.
