  Surgical instrument segmentation is recognised as a key enabler to provide
advanced surgical assistance and improve computer assisted interventions. In
this work, we propose SegMatch, a semi supervised learning method to reduce the
need for expensive annotation for laparoscopic and robotic surgical images.
SegMatch builds on FixMatch, a widespread semi supervised classification
pipeline combining consistency regularization and pseudo labelling, and adapts
it for the purpose of segmentation. In our proposed SegMatch, the unlabelled
images are weakly augmented and fed into the segmentation model to generate a
pseudo-label to enforce the unsupervised loss against the output of the model
for the adversarial augmented image on the pixels with a high confidence score.
Our adaptation for segmentation tasks includes carefully considering the
equivariance and invariance properties of the augmentation functions we rely
on. To increase the relevance of our augmentations, we depart from using only
handcrafted augmentations and introduce a trainable adversarial augmentation
strategy. Our algorithm was evaluated on the MICCAI Instrument Segmentation
Challenge datasets Robust-MIS 2019 and EndoVis 2017. Our results demonstrate
that adding unlabelled data for training purposes allows us to surpass the
performance of fully supervised approaches which are limited by the
availability of training data in these challenges. SegMatch also outperforms a
range of state-of-the-art semi-supervised learning semantic segmentation models
in different labelled to unlabelled data ratios.
