Jiachen Wu,1,2 , Robert Kuschmierz,2 Ortrud Uckermann,3 Roberta Galli,3 Gabriele Schackert,3 Liangcai Caohttps://orcid.org/0000-0002-8099-2948,1 Jürgen Czarske2
1Tsinghua Univ. (China) 2TU Dresden (Germany) 3Universitätsklinikum Carl Gustav Carus Dresden (Germany)
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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, 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