Quantitative Phase Imaging Images Research Articles

CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging.

Three-dimensional quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. It has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw three-dimensional (3D) tomograms is not well-developed. We focus on a critical, yet often underappreciated, step of the analysis pipeline that of 3D cell segmentation from the acquired tomograms. We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the 3D segmentation of QPI images. The cell segmentation algorithm mimics the gemstone extraction process, initiating with a coarse 3D extrusion from a two-dimensional (2D) segmented mask to outline the cell structure. A 2D image is generated, and a segmentation algorithm identifies the boundary in the plane. Leveraging cell continuity in consecutive -stacks, a refined 3D segmentation, akin to fine chiseling in gemstone carving, completes the process. The CellSNAP algorithm outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2s per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused AI-based segmentation tools. Our proposed method is less memory intensive and significantly faster than existing methods. The method can be easily implemented on a student laptop. Since the approach is rule-based, there is no need to collect a lot of imaging data and manually annotate them to perform machine learning based training of the model. We envision our work will lead to broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.

Common-path digital holographic microscopy based on a volume holographic grating for quantitative phase imaging.

Digital holographic microscopy (DHM) is a powerful quantitative phase imaging (QPI) technique that is capable of recording sample's phase information to enhance image contrast. In off-axis DHM, high-quality QPI images can be generated within a single recorded hologram, and the system stability can be enhanced by common-path configuration. Diffraction gratings are widely used components in common-path DHM systems; however, the presence of multiple diffraction beams leads to system power loss. Here, we propose and demonstrate implementation of a volume holographic grating (VHG) in common-path DHM, which provides single diffraction order. VHG in common-path DHM (i.e., VHG-DHM) helps in improving signal-to-noise ratio as compared to the conventional DHM. In addition, VHG, with inherently high angular selectivity, reduces image noise caused by stray light. With a simple fabrication process, it is convenient to utilize VHG to control the beam separation angle of DHM. Further, by using Bragg-matched wavelength degeneracy to avoid potential cell damaging effect in blue light, the VHG is designed for recording at a maximum sensitive wavelength of ∼488 nm, while our VHG-DHM is operated at the longer wavelength of red 632.8 nm for cell observation. Experimental results, measured by the VHG-DHM, show the measurement of target thickness ranging from 100 nm to 350 nm. In addition, stability of the system is quantitatively measured. High-contrast QPI images of human lung cancer cells are demonstrated.

Investigating the Joint Amplitude and Phase Imaging of Stained Samples in Automatic Diagnosis.

The diagnosis of many diseases relies, at least on first intention, on an analysis of blood smears acquired with a microscope. However, image quality is often insufficient for the automation of such processing. A promising improvement concerns the acquisition of enriched information on samples. In particular, Quantitative Phase Imaging (QPI) techniques, which allow the digitization of the phase in complement to the intensity, are attracting growing interest. Such imaging allows the exploration of transparent objects not visible in the intensity image using the phase image only. Another direction proposes using stained images to reveal some characteristics of the cells in the intensity image; in this case, the phase information is not exploited. In this paper, we question the interest of using the bi-modal information brought by intensity and phase in a QPI acquisition when the samples are stained. We consider the problem of detecting parasitized red blood cells for diagnosing malaria from stained blood smears using a Deep Neural Network (DNN). Fourier Ptychographic Microscopy (FPM) is used as the computational microscopy framework to produce QPI images. We show that the bi-modal information enhances the detection performance by 4% compared to the intensity image only when the convolution in the DNN is implemented through a complex-based formalism. This proves that the DNN can benefit from the bi-modal enhanced information. We conjecture that these results should extend to other applications processed through QPI acquisition.

Comparing the segmentation of quantitative phase images of neurons using convolutional neural networks trained on simulated and augmented imagery.

Quantitative phase imaging (QPI) can visualize cellular morphology and measure dry mass. Automated segmentation of QPI imagery is desirable for tracking neuron growth. Convolutional neural networks (CNNs) have provided state-of-the-art results for image segmentation. Improving the amount and robustness of training data is often crucial to improving CNN output on novel samples, but acquiring enough labeled data can be labor intensive. Data augmentation and simulation can be used to address this, but it is unclear whether low-complexity data can result in useful network generalization. We trained CNNs on abstract images of neurons and on augmented images of real neurons. We then benchmarked the resulting models against human labeling. We used a stochastic simulation of neuron growth to guide abstract QPI image and label generation. We then tested the segmentation performance of networks trained on augmented data and networks trained on simulated data against manual labeling established via consensus of three human labelers. We show that training on augmented real data resulted in a model that achieved the best Dice coefficients in our group of CNNs. The largest percent difference in dry mass estimation with respect to the ground truth was driven by segmentation errors of cell debris and phase noise. The error in dry mass when considering the cell body alone was similar between the CNNs. Neurite pixels only accounted for of the total image space, making them a difficult feature to learn. Future efforts should consider methods for improving neurite segmentation quality. Augmented data outperformed the simulated abstract data for this testing set. The quality of segmentation of neurites was the key difference in performance between the models. Notably, even humans performed poorly when segmenting neurites. Further work is needed to improve the segmentation quality of neurites.

Quantitative Phase Imaging Using Digital Holographic Microscopy to Assess the Degree of Intestinal Inflammation in Patients with Ulcerative Colitis.

Ulcerative colitis (UC) is characterized by chronic inflammation of the colorectum. Histological remission has emerged as a potential future treatment goal; however, the histopathological assessment of intestinal inflammation in UC remains challenging with a multitude of available scoring systems and the need for a pathologist with expertise in inflammatory bowel disease (IBD). In previous studies, quantitative phase imaging (QPI) including digital holographic microscopy (DHM) was successfully applied as an objective method for stain-free quantification of the degree of inflammation in tissue sections. Here, we evaluated the application of DHM for the quantitative assessment of histopathological inflammation in patients with UC. In our study, endoscopically obtained colonic and rectal mucosal biopsy samples from 21 patients with UC were analyzed by capturing DHM-based QPI images that were subsequently evaluated using the subepithelial refractive index (RI). The retrieved RI data were correlated with established histological scoring systems including the Nancy index (NI) as well as with endoscopic and clinical findings. As a primary endpoint, we found a significant correlation between the DHM-based retrieved RI and the NI (R2 = 0.251, p < 0.001). Furthermore, RI values correlated with the Mayo endoscopic subscore (MES; R2 = 0.176, p < 0.001). An area under the receiver operating characteristics (ROC) curve of 0.820 confirms the subepithelial RI as a reliable parameter to distinguish biopsies with histologically active UC from biopsies without evidence of active disease as determined by conventional histopathological examination. An RI higher than 1.3488 was found to be the most sensitive and specific cut-off value to identify histologically active UC (sensitivity of 84% and specificity of 72%). In conclusion, our data demonstrate DHM to be a reliable tool for the quantitative assessment of mucosal inflammation in patients with UC.

Multi-contrast digital histopathology of mouse organs using quantitative phase imaging and virtual staining.

Quantitative phase imaging (QPI) has emerged as a new digital histopathologic tool as it provides structural information of conventional slide without staining process. It is also capable of imaging biological tissue sections with sub-nanometer sensitivity and classifying them using light scattering properties. Here we extend its capability further by using optical scattering properties as imaging contrast in a wide-field QPI. In our first step towards validation, QPI images of 10 major organs of a wild-type mouse have been obtained followed by H&E-stained images of the corresponding tissue sections. Furthermore, we utilized deep learning model based on generative adversarial network (GAN) architecture for virtual staining of phase delay images to a H&E-equivalent brightfield (BF) image analogues. Using the structural similarity index, we demonstrate similarities between virtually stained and H&E histology images. Whereas the scattering-based maps look rather similar to QPI phase maps in the kidney, the brain images show significant improvement over QPI with clear demarcation of features across all regions. Since our technology provides not only structural information but also unique optical property maps, it could potentially become a fast and contrast-enriched histopathology technique.

Quantifying Subcellular Growth Dynamics using Quantitative Phase Imaging

Unregulated growth has a drastic effect on the health of organisms. Unbalanced biomass production and degradation within cells can lead to diseases including diabetes, cancer, and organ hypertrophy. Understanding such growth dysregulations and the development of treatments requires understanding growth. Average growth of cell populations can be measured based on proliferation, while more advanced growth measurement approaches study single cells and can extract cell-to-cell heterogeneity information. We have developed a technique that extends further to measure localization of growth within single cells, using quantitative phase imaging (QPI) microscopy technique.QPI measures the phase shift of light as it passes through a sample, which is linearly proportional to cell biomass in the light path. As a result, QPI data show the changing biomass distribution within cells. Cell growth is commonly measured by QPI as the rate of change of biomass in whole cells over time. This approach has previously been shown to rapidly measure single cell growth and growth inhibition by drugs or therapies. Our work expands the scope of QPI further with an algorithm that utilizes the biomass distribution data to output a discretized subcellular growth map.Our approach is based on solution of a mass balance that considers biomass advection in subcellular regions. Observed changes in mass within each region of the cell can be caused by either advection of mass or biosynthesis/degradation. We can directly measure the change in biomass in each subcellular volume using QPI. We then measure the velocity of mass movement using particle image velocimetry on QPI images. Combining these two allows us to measure the amount of biosynthesis in each subcellular region. Our approach visualizes subcellular growth dynamics in real-time and quantify intracellular growth inhibition in response to drug treatments targeting cell growth mechanisms.

Practical application of microsphere samples for benchmarking a quantitative phase imaging system.

Quantitative phase imaging (QPI) provides an approach for monitoring the dry mass of individual cells by measuring the optical pathlength of visible light as it passes through cells. A distinct advantage of QPI is that the measurements result in optical path length quantities that are, in principle, instrument independent. Reference materials that induce a well‐defined optical pathlength shift and are compatible with QPI imaging systems will be valuable in assuring the accuracy of such measurements on different instruments. In this study, we evaluate seven combinations of microspheres embedded in index refraction matching media as candidate reference materials for benchmarking the performance of a QPI system and as calibration standards for the optical pathlength measurement. Poly(methyl metharylate) microspheres and mineral oil were used to evaluate the range of illumination apertures, signal‐to‐noise ratios, and focus positions that allow an accurate quantitative optical pathlength measurement. The microsphere‐based reference material can be used to verify settings on an instrument that are suitable for obtaining an accurate pathlength measurement from biological cells. The microsphere/media reference material is applied to QPI‐based dry mass measurements of a population of HEK293 cells to benchmark and provide evidence that the QPI image data are accurate.

High-resolution label-free imaging of tissue morphology with confocal phase microscopy

Label-free imaging approaches seek to simplify and augment histopathologic assessment by replacing the current practice of staining by dyes to visualize tissue morphology with quantitative optical measurements. Quantitative phase imaging (QPI) operates with visible/UV light and thus provides a resolution matched to current practice. Here we introduce and demonstrate confocal QPI for label-free imaging of tissue sections and assess its utility for manual histopathologic inspection. Imaging cancerous and normal adjacent human breast and prostate, we show that tissue structural organization can be resolved with high spatial detail comparable to conventional hematoxylin and eosin (H&amp;E) stains. Our confocal QPI images are found to be free of halo, solving this common problem in QPI. We further describe a virtual imaging system based on finite-difference time-domain (FDTD) calculations and combine it with numerical tissue phantoms to quantitatively show the absence of halo and the improved clarity in resolving subcellular features with confocal QPI compared to wide-field QPI. Confocal QPI bears the potential to become a common tool for label-free disease diagnosis, while the presented FDTD method provides a flexible platform to evaluate the diagnostic potential of QPI methods.

Invited Article: Digital refocusing in quantitative phase imaging for flowing red blood cells.

Quantitative phase imaging (QPI) offers high optical path length sensitivity, probing nanoscale features of live cells, but it is typically limited to imaging just few static cells at a time. To enable utility as a biomedical diagnostic modality, higher throughput is needed. To meet this need, methods for imaging cells in flow using QPI are in development. An important need for this application is to enable accurate quantitative analysis. However, this can be complicated when cells shift focal planes during flow. QPI permits digital refocusing since the complex optical field is measured. Here we analyze QPI images of moving red blood cells with an emphasis on choosing a quantitative criterion for digitally refocusing cell images. Of particular interest is the influence of optical absorption which can skew refocusing algorithms. Examples of refocusing of holographic images of flowing red blood cells using different approaches are presented and analyzed.

Quantitative phase imaging for cell culture quality control.

The potential of quantitative phase imaging (QPI) with digital holographic microscopy (DHM) for quantification of cell culture quality was explored. Label-free QPI of detached single cells in suspension was performed by Michelson interferometer-based self-interference DHM. Two pancreatic tumor cell lines were chosen as cellular model and analyzed for refractive index, volume, and dry mass under varying culture conditions. Firstly, adequate cell numbers for reliable statistics were identified. Then, to characterize the performance and reproducibility of the method, we compared results from independently repeated measurements and quantified the cellular response to osmolality changes of the cell culture medium. Finally, it was demonstrated that the evaluation of QPI images allows the extraction of absolute cell parameters which are related to cell layer confluence states. In summary, the results show that QPI enables label-free imaging cytometry, which provides novel complementary integral biophysical data sets for sophisticated quantification of cell culture quality with minimized sample preparation. © 2017 International Society for Advancement of Cytometry.