Exploiting Deep Learning and Lensless Optics for Intraoperative Real-Time Diagnostics and Optogenetics
Abstract: Light has the potential to recognize the origins of diseases, to prevent them, or to cure them early and gently. The early diagnosis is the key to improve the survival rate and cure rate of patients. Endoscopy plays an important role in the early stages of diagnosis by guiding biopsy extraction. Conventionally, it takes several hours to a few days for the surgeon to know the results of the diagnosis. Optical biopsy offers real-time intraoperative diagnosis and reduces the risk of surgical resection.
The state of the art are fiber endoscopes, but the lens optics limits the size. Lensless multi-core fiber endoscopy offers both small diameters of a few 100 microns and the suitability as single-use probes, which is beneficial in sterilization. We demonstrate an end-to-end lensless fiber imaging using deep neural networks. The well-trained resolution enhancement network not only recovers high-resolution features beyond the physical limitations, but also helps improving tumor recognition rate. This paradigm shift enables imaging with lensless fiber endoscopy and is promising especially for minimally invasive intraoperative cancer diagnosis and optogenetics.
About: Juergen Czarske (Fellow Optica, SPIE, EOS, IET) is professor and director based at TU Dresden. His awards include the 1996 AHMT Measurement Technique Prize (München), the 2008 Berthold Leibinger Innovation Prize (Ditzingen), the 2019 OSA Joseph Fraunhofer Award/Robert M. Burley Prize (Washington DC), the 2020 Laser Instrumentation Award of IEEE Photonics Society (New York City) and the SPIE Community Champion 2020 (Washington).