  Anomaly detection in Minimally-Invasive Surgery (MIS) traditionally requires
a human expert monitoring the procedure from a console. Data scarcity, on the
other hand, hinders what would be a desirable migration towards autonomous
robotic-assisted surgical systems. Automated anomaly detection systems in this
area typically rely on classical supervised learning. Anomalous events in a
surgical setting, however, are rare, making it difficult to capture data to
train a detection model in a supervised fashion. In this work we thus propose
an unsupervised approach to anomaly detection for robotic-assisted surgery
based on deep residual autoencoders. The idea is to make the autoencoder learn
the 'normal' distribution of the data and detect abnormal events deviating from
this distribution by measuring the reconstruction error. The model is trained
and validated upon both the publicly available Cholec80 dataset, provided with
extra annotation, and on a set of videos captured on procedures using
artificial anatomies ('phantoms') produced as part of the Smart Autonomous
Robotic Assistant Surgeon (SARAS) project. The system achieves recall and
precision equal to 78.4%, 91.5%, respectively, on Cholec80 and of 95.6%, 88.1%
on the SARAS phantom dataset. The end-to-end system was developed and deployed
as part of the SARAS demonstration platform for real-time anomaly detection
with a processing time of about 25 ms per frame.
