More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation
This work addresses data planning strategies for surgical guidance applications, offering practical insights for clinical settings where image acquisition and expert labelling are costly, though it is incremental in nature.
The study investigated the trade-off between adding more unlabelled data versus labelling more data for semi-supervised laparoscopic liver image segmentation, finding that the semi-supervised approach significantly improved segmentation accuracy compared to supervised learning, with the training strategy contributing to this gain.
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.