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arXiv · 2307.11638

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
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Abstract

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.

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Charlie Budd, Jianrong Qiu, Oscar MacCormac, Martin Huber, Christopher Mower, Mirek Janatka, Théo Trotouin, Jonathan Shapey, Mads S. Bergholt, Tom Vercauteren. 2023-07-21. Deep Reinforcement Learning Based System for Intraoperative Hyperspectral Video Autofocusing. https://doi.org/10.1007/978-3-031-43996-4_63

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