  The introduction of surgical robots brought about advancements in surgical
procedures. The applications of remote telesurgery range from building medical
clinics in underprivileged areas, to placing robots abroad in military
hot-spots where accessibility and diversity of medical experience may be
limited. Poor wireless connectivity may result in a prolonged delay, referred
to as latency, between a surgeon's input and action a robot takes. In surgery,
any micro-delay can injure a patient severely and in some cases, result in
fatality. One was to increase safety is to mitigate the effects of latency
using deep learning aided computer vision. While the current surgical robots
use calibrated sensors to measure the position of the arms and tools, in this
work we present a purely optical approach that provides a measurement of the
tool position in relation to the patient's tissues. This research aimed to
produce a neural network that allowed a robot to detect its own mechanical
manipulator arms. A conditional generative adversarial networks (cGAN) was
trained on 1107 frames of mock gastrointestinal robotic surgery data from the
2015 EndoVis Instrument Challenge and corresponding hand-drawn labels for each
frame. When run on new testing data, the network generated near-perfect labels
of the input images which were visually consistent with the hand-drawn labels
and was able to do this in 299 milliseconds. These accurately generated labels
can then be used as simplified identifiers for the robot to track its own
controlled tools. These results show potential for conditional GANs as a
reaction mechanism such that the robot can detect when its arms move outside
the operating area within a patient. This system allows for more accurate
monitoring of the position of surgical instruments in relation to the patient's
tissue, increasing safety measures that are integral to successful telesurgery
systems.
