With the advances in deep learning analysis a question arises – if for a robust network learning cycle, the shift between raw data and image-based data, is of importance. In the current work, we start exploring this topic by focusing on the following case study: categorizing cancer cells from holographic images. The problem in the categorization process is the time consumption of the transformation from off-axis image holograms to OPD maps. While there have been attempts for fast transformation, they still take non-negligible time. We propose a novel approach for fast classification that skips the pre-processing of creating OPD maps and directly uses the raw data of holographic images. Our dataset contains two separate image acquisitions of primary cancer cells (SW480) and metastatic cancer cells (SW620) of colorectal adenocarcinoma imaged during flow. We extracted the OPD maps of those cells and used them to train and evaluate a ResNet model to create baseline results. Our convolutional neural network (CNN) model is based on Y-Net approach: during training synthetic OPD images are created from input holograms, using the real OPD maps while simultaneously classifying the cells. During inference time only the classification branch operates to further reduce running time. This approach saves computational time by over 90%.
We present highly dynamic photothermal interferometric phase microscopy for quantitative, selective contrast imaging of live cells during flow. Gold nanoparticles can be biofunctionalized to bind to specific cells, and stimulated for local temperature increase due to plasmon resonance, causing a rapid change of the optical phase. These phase changes can be recorded by interferometric phase microscopy and analyzed to form an image of the binding sites of the nanoparticles in the cells, gaining molecular specificity. Since the nanoparticle excitation frequency might overlap with the sample dynamics frequencies, photothermal phase imaging was performed on stationary or slowly dynamic samples. Furthermore, the computational analysis of the photothermal signals is time consuming. This makes photothermal imaging unsuitable for applications requiring dynamic imaging or real-time analysis, such as analyzing and sorting cells during fast flow. To overcome these drawbacks, we utilized an external interferometric module and developed new algorithms, based on discrete Fourier transform variants, enabling fast analysis of photothermal signals in highly dynamic live cells. Due to the self-interference module, the cells are imaged with and without excitation in video-rate, effectively increasing signal-to-noise ratio. Our approach holds potential for using photothermal cell imaging and depletion in flow cytometry.
Measurements of biological cells during flow are highly important for medical diagnosis based on cell sorting. In the case of cell imaging during flow, very rapid image acquisition capabilities are required to enable fast cell flow for analyzing a sufficient number of cells. We present a new flipping interferometry (FI) module for simplified off-axis close-to-common-path interferometric phase microscopy. This wide-field off-axis interferometric module provides rapid quantitative phase microscopy of biological cells during flow in a microfluidic channel, with potential of integration into cell sorting devices. Various experimental demonstrations are presented.
We present a new analysis tool for studying texture changes in the quantitative phase maps of live cells acquired by wide-field interferometry. The sensitivity of wide-field interferometry systems to small changes in refractive index enables visualizing cells and inner cell organelles without the using fluorescent dyes or other cell-invasive approaches, which may affect the measurement and require external labeling. Our label-free texture-analysis tool is based directly on the optical path delay profile of the sample and does not necessitate decoupling refractive index and thickness in the cell quantitative phase profile; thus, relevant parameters can be calculated using a single-frame acquisition. Our experimental system includes low-coherence wide-field interferometer, combined with simultaneous florescence microscopy system for validation. We used this system and analysis tool for studying lipid droplets formation in adipocytes. The latter demonstration is relevant for various cellular functions such as lipid metabolism, protein storage and degradation to viral replication. These processes are functionally linked to several physiological and pathological conditions, including obesity and metabolic diseases. Quantification of these biological phenomena based on the texture changes in the cell phase map has a potential as a new cellular diagnosis tool.
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