More Unlabelled Data or Label More Data? A Study on Semi-supervised Laparoscopic Image Segmentation
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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.
KeywordsSemi-supervised Laparoscopic video Image segmentation
This work is supported by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) (203145Z/16/Z). DS receives funding from EPSRC [EP/P012841/1]. MC receives funding from EPSRC [EP/P034454/1]. BD was supported by the NIHR Biomedical Research Centre at University College London Hospitals NHS Foundations Trust and University College London. The imaging data used for this work were obtained with funding from the Health Innovation Challenge Fund [HICF-T4-317], a parallel funding partnership between the Wellcome Trust and the Department of Health.
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