Deep Reinforcement Learning Based System for Intraoperative
  Hyperspectral Video Autofocusing
Charlie Budd Jianrong Qiu Oscar MacCormac Martin Huber Christopher Mower Mirek Janatka Théo Trotouin Jonathan Shapey Mads S. Bergholt Tom Vercauteren
Hyperspectral imaging (HSI) captures a greater level of spectral detail than
traditional optical imaging, making it a potentially valuable intraoperative
tool when precise tissue differentiation is essential. Hardware limitations of
current optical systems used for handheld real-time video HSI result in a
limited focal depth, thereby posing usability issues for integration of the
technology into the operating room. This work integrates a focus-tunable liquid
lens into a video HSI exoscope, and proposes novel video autofocusing methods
based on deep reinforcement learning. A first-of-its-kind robotic focal-time
scan was performed to create a realistic and reproducible testing dataset. We
benchmarked our proposed autofocus algorithm against traditional policies, and
found our novel approach to perform significantly ($p<0.05$) better than
traditional techniques ($0.070\pm.098$ mean absolute focal error compared to
$0.146\pm.148$). In addition, we performed a blinded usability trial by having
two neurosurgeons compare the system with different autofocus policies, and
found our novel approach to be the most favourable, making our system a
desirable addition for intraoperative HSI.