Deep learning techniques are always bound with big data and large, sophisticated models. In this paper, we show that this is not necessarily true for the task of end-to-end phase retrieval in off-axis interferometric quantitative phase imaging. For this task, we first introduce a new loss function, called bucket error rate (BER), for addressing the problem of imbalanced data distribution by balancing loss-bias of target and background area adaptively. With BER, we demonstrate that a U-Net model can learn the underneath logic for converting a raw interferogram to a phase map from only one training sample. At last, we present a novel mixed-context network (MCN) which can simultaneously aggregate local- and global-contextual information. Experimental results show that compared to U-Net, the proposed MCN is more accurate, more compact, and can be trained faster.
Real-time quantitative phase imaging is beneficial for observation and analysis of living cells. Despite off-axis interferometry-based quantitative phase microscopy (off-axis QPM) offers single-shot image acquisition, it usually requires a calibration image captured at a blank field of view to correct the aberration and a multi-step processing algorithm to reconstruct a phase map. Therefore, it is challenging to achieve real-time phase imaging. To simplify experimental operations and expedite image processing, we propose a lightweight U-Net based deep neural network for calibration-free and fast phase retrieval in off-axis QPM. Output phase maps of the lightweight U-Net achieve high fidelity with an average Structural SIMilarity (SSIM) index value of 90.2%. Via running this lightweight U-Net model on a laptop connected with a portable QPM system, we demonstrate an ease-of-use and compact QPM method that can be used for real-time imaging of living cells.
Quantitative phase microscopy (QPM) has been successfully applied to studying the biophysical properties of red blood cells (RBCs) in a label-free and high-throughput way. However, the lack of molecular specificity has hindered the applicability of QPM for further studies in RBCs. In this paper, we propose a compact and three-wavelength QPM method and demonstrate its potential for measuring molecular-specific properties of specimens. Using the quantitative phase images from three wavelength channels that are acquired in a single shot, we derive a model to obtain the oxyhemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) concentrations in RBCs. This new advance in QPM could be further applied to studying the morph-molecular properties of cells in real-time and characterizing cells in large populations to enable more frontier biomedical investigations.
KEYWORDS: Microscopes, Embedded systems, Imaging systems, Image transmission, Image processing, Control systems, Cameras, Power supplies, Mobile devices, Local area networks
Wireless communication can break the limitations of space and enable data transferring between disconnected equipment. Automatic and remote-controlled experimental equipment is required for factories or when it is inconvenient to work in the lab, e.g., during the pandemic. We implemented and demonstrated a wireless and automated quantitative phase microscope (QPM) system which can be used for observation of samples remotely or in a confined space. Microscopic manipulations, such as sample placement and scanning, can be operated through a robotic arm and motorized stages controlled by an embedded computing board equipped with a Wi-Fi connection.
We demonstrate a new portable multi-wavelength fiber-based quantitative phase microscope (FQPM). In FQPM, the reference beam is made to propagate in a single-mode optical fiber whose length is tuned to ensure the best interference contrast with the sample beam. For further compactness, different illumination wavelengths are multiplexing through fiber combiners and couplers. FQPM simultaneously acquires the sample interferograms coming from two illumination wavelengths in a single shot, and their corresponding phase maps can be obtained separately. We have used FQPM for imaging dispersive samples, such as fluorescence particles and red blood cells and further analyze the refractive index and intracellular hemoglobin concentration.
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