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- ItemComputational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning(Washington, DC : OSA, 2021) Pradhan, Pranita; Meyer, Tobias; Vieth, Michael; Stallmach, Andreas; Waldner, Maximilian; Schmitt, Michael; Popp, Juergen; Bocklitz, ThomasHematoxylin and Eosin (H&E) staining is the 'gold-standard' method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for 'real-time' disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author's best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner.
- ItemFLIM data analysis based on Laguerre polynomial decomposition and machine-learning(Bellingham, Wash. : SPIE, 2021) Guo, Shuxia; Silge, Anja; Bae, Hyeonsoo; Tolstik, Tatiana; Meyer, Tobias; Matziolis, Georg; Schmitt, Michael; Popp, Jürgen; Bocklitz, ThomasSignificance: The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis. Aim: We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML). Approach: The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker. Results: The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components.
- ItemDeep learning as phase retrieval tool for CARS spectra(Washington, DC : Soc., 2020) Houhou, Rola; Barman, Parijat; Schmitt, Micheal; Meyer, Tobias; Popp, Juergen; Bocklitz, ThomasFinding efficient and reliable methods for the extraction of the phase in optical measurements is challenging and has been widely investigated. Although sophisticated optical settings, e.g. holography, measure directly the phase, the use of algorithmic methods has gained attention due to its efficiency, fast calculation and easy setup requirements. We investigated three phase retrieval methods: the maximum entropy technique (MEM), the Kramers-Kronig relation (KK), and for the first time deep learning using the Long Short-Term Memory network (LSTM). LSTM shows superior results for the phase retrieval problem of coherent anti-Stokes Raman spectra in comparison to MEM and KK. © 2020 OSA - The Optical Society. All rights reserved.
- ItemUltrafast intermodal third harmonic generation in a liquid core step-index fiber filled with C2Cl4(Washington, DC : Soc., 2020) Schaarschmidt, Kay; Kobelke, Jens; Nolte, Stefan; Meyer, Tobias; Schmidt, Markus A.Third harmonic generation in a circular liquid core step-index fiber filled with a highly transparent inorganic solvent is demonstrated experimentally using ultrafast pump pulses of different durations in the telecom domain for the first time. Specifically we achieve intermodal phase matching to the HE13 higher order mode at the harmonic wavelength and found clear indications of a non-instantaneous molecular contribution to the total nonlinearity in the spectral broadening of the pump. Spectral power evolution and efficiency of the conversion process is studied for all pulse parameters, while we found the greatest photon yield for the longest pulses as well as an unexpected blue-shift of the third harmonic wavelength with increasing pump power. Our results provide the basis for future studies aiming at using this tunable fiber platform with a sophisticated nonlinear response in the context of harmonic generation. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
- ItemUltrafast intermodal third harmonic generation in a liquid core step-index fiber filled with C2Cl4: erratum(Washington, DC : Soc., 2021) Schaarschmidt, Kay; Kobelke, Jens; Nolte, Stefan; Meyer, Tobias; Schmidt, Markus A.We provide a correction due to an erroneous repetition rate of one of the laser systems (90 fs pulse duration) in our previously published paper [Opt. Express28, 25037 (2020)10.1364/OE.399771].
- ItemSupercontinuum generation in a carbon disulfide core microstructured optical fiber(Washington, DC : Soc., 2021) Junaid, Saher; Bierlich, Joerg; Hartung, Alexander; Meyer, Tobias; Chemnitz, Mario; Schmidt, Markus A.We demonstrate supercontinuum generation in a liquid-core microstructured optical fiber using carbon disulfide as the core material. The fiber provides a specific dispersion landscape with a zero-dispersion wavelength approaching the telecommunication domain where the corresponding capillary-type counterpart shows unsuitable dispersion properties for soliton fission. The experiments were conducted using two pump lasers with different pulse duration (30 fs and 90 fs) giving rise to different non-instantaneous contributions of carbon disulfide in each case. The presented results demonstrate an extraordinary high conversion efficiency from pump to soliton and to dispersive wave, overall defining a platform that enables studying the impact of non-instantaneous responses on ultrafast soliton dynamics and coherence using straightforward pump lasers and diagnostics.
- ItemA rigid coherent anti-Stokes Raman scattering endoscope with high resolution and a large field of view(College Park : American Institute of Physics, 2018) Zirak, P.; Matz, Gregor; Messerschmidt, Bernhard; Meyer, Tobias; Schmitt, Michael; Popp, Jürgen; Uckermann, Ortrud; Galli, R.; Kirsch, Matthias; Winterhalder, M.J.; Zumbusch, A.Nonlinear optical endoscopy is an attractive technique for biomedical imaging since it promises to give access to high resolution imaging in vivo. Among the various techniques used for endoscopic contrast generation, coherent anti-Stokes Raman scattering (CARS) is especially interesting. CARS endoscopy allows molecule specific imaging of unlabeled samples. In this contribution, we describe the design, implementation, and experimental characterization of a rigid, compact CARS endoscope with a spatial resolution of 750 nm over a field of view of roughly 250 μm. Omission of the relay optics and use of a gradient index lens specifically designed for this application allow one to realize these specifications in an endoscopic unit which is 2.2 mm wide over a length of 187 mm, making clinical applications during surgical interventions possible. Multimodal use of the endoscope is demonstrated with images of samples with neurosurgical relevance.Nonlinear optical endoscopy is an attractive technique for biomedical imaging since it promises to give access to high resolution imaging in vivo. Among the various techniques used for endoscopic contrast generation, coherent anti-Stokes Raman scattering (CARS) is especially interesting. CARS endoscopy allows molecule specific imaging of unlabeled samples. In this contribution, we describe the design, implementation, and experimental characterization of a rigid, compact CARS endoscope with a spatial resolution of 750 nm over a field of view of roughly 250 μm. Omission of the relay optics and use of a gradient index lens specifically designed for this application allow one to realize these specifications in an endoscopic unit which is 2.2 mm wide over a length of 187 mm, making clinical applications during surgical interventions possible. Multimodal use of the endoscope is demonstrated with images of samples with neurosurgical relevance.