  Instance segmentation of surgical instruments is a long-standing research
problem, crucial for the development of many applications for computer-assisted
surgery. This problem is commonly tackled via fully-supervised training of deep
learning models, requiring expensive pixel-level annotations to train. In this
work, we develop a framework for instance segmentation not relying on spatial
annotations for training. Instead, our solution only requires binary tool
masks, obtainable using recent unsupervised approaches, and binary tool
presence labels, freely obtainable in robot-assisted surgery. Based on the
binary mask information, our solution learns to extract individual tool
instances from single frames, and to encode each instance into a compact vector
representation, capturing its semantic features. Such representations guide the
automatic selection of a tiny number of instances (8 only in our experiments),
displayed to a human operator for tool-type labelling. The gathered information
is finally used to match each training instance with a binary tool presence
label, providing an effective supervision signal to train a tool instance
classifier. We validate our framework on the EndoVis 2017 and 2018 segmentation
datasets. We provide results using binary masks obtained either by manual
annotation or as predictions of an unsupervised binary segmentation model. The
latter solution yields an instance segmentation approach completely free from
spatial annotations, outperforming several state-of-the-art fully-supervised
segmentation approaches.

Computer Vision and Pattern Recognition