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20 May 2022 Learning-based high-resolution lensless fiber bundle imaging for tumor
Jiachen Wu, , Robert Kuschmierz, Ortrud Uckermann, Roberta Galli, Gabriele Schackert, Liangcai Cao, Jürgen Czarske
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Proceedings Volume PC12136, Unconventional Optical Imaging III; PC121360W (2022) https://doi.org/10.1117/12.2624170
Event: SPIE Photonics Europe, 2022, Strasbourg, France
Abstract
Fiber-based lensless endoscopy is powerful tool for minimally invasive tissue in clinical practice. However, the inherent honeycomb-artifact reduce the resolution and increases diagnosis difficulty. We proposed an end-to-end resolution enhancement and classification network for fiber bundle imaging. Comparing with conventional interpolation and filtering methods, the average peak signal to noise ratio (PSNR) can be improved 2~6 dB. Then we trained a VGG-19 classification network on label-free multiphoton images of 382 human braintumor 28 nontumor brain samples. The results show the classification accuracy of enhanced images is up to 91%, while the fiber bundle images are only 67% accurate. The method paves the way to in vivo histologic imaging through miniaturized endoscopic probes, and gives rapid and accurate determination for intraoperative diagnosis.
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Jiachen Wu, , Robert Kuschmierz, Ortrud Uckermann, Roberta Galli, Gabriele Schackert, Liangcai Cao, and Jürgen Czarske "Learning-based high-resolution lensless fiber bundle imaging for tumor", Proc. SPIE PC12136, Unconventional Optical Imaging III, PC121360W (20 May 2022); https://doi.org/10.1117/12.2624170
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KEYWORDS
Tumors
Image enhancement
Endoscopes
Brain
Image classification
Neuroimaging
Optical imaging
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