  Improving a semi-supervised image segmentation task has the option of adding
more unlabelled images, labelling the unlabelled images or combining both, as
neither image acquisition nor expert labelling can be considered trivial in
most clinical applications. With a laparoscopic liver image segmentation
application, we investigate the performance impact by altering the quantities
of labelled and unlabelled training data, using a semi-supervised segmentation
algorithm based on the mean teacher learning paradigm. We first report a
significantly higher segmentation accuracy, compared with supervised learning.
Interestingly, this comparison reveals that the training strategy adopted in
the semi-supervised algorithm is also responsible for this observed
improvement, in addition to the added unlabelled data. We then compare
different combinations of labelled and unlabelled data set sizes for training
semi-supervised segmentation networks, to provide a quantitative example of the
practically useful trade-off between the two data planning strategies in this
surgical guidance application.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Machine Learning