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More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation

Authors :
Fu, Yunguan
Robu, Maria R.
Koo, Bongjin
Schneider, Crispin
van Laarhoven, Stijn
Stoyanov, Danail
Davidson, Brian
Clarkson, Matthew J.
Hu, Yipeng
Publication Year :
2019

Abstract

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.<br />Comment: Accepted to MICCAI MIL3ID 2019

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.1908.08035
Document Type :
Working Paper