Coherence Tomography Microscopy Research Articles

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

Background and ObjectivesTechniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel. MethodsTo construct the cGAN training and simulated test sets, we generated 30,000 artificial elasticity images using a parametric model and computed the corresponding phase difference images using finite element analysis to simulate compression applied to the artificial samples. We also imaged real MCF7 breast tumor spheroids embedded in hydrogel using mechano-microscopy to construct the experimental test set and evaluated the cGAN using the algebraic elasticity images and co-registered OCM and confocal fluorescence microscopy (CFM) images. ResultsComparison with the simulated test set ground truth elasticity images shows the cGAN produces a lower root mean square error (median: 3.47 kPa, 95 % confidence interval (CI) [3.41, 3.52]) than the algebraic method (median: 4.91 kPa, 95 % CI [4.85, 4.97]). For the experimental test set, the cGAN elasticity images contain features resembling stiff nuclei at locations corresponding to nuclei seen in the algebraic elasticity, OCM, and CFM images. Furthermore, the cGAN elasticity images are higher resolution and more robust to noise than the algebraic elasticity images. ConclusionsThe cGAN elasticity images exhibit better accuracy, spatial resolution, sensitivity, and robustness to noise than the algebraic elasticity images for both simulated and real experimental data.

Attention LSTM U-Net model for Drosophila melanogaster heart tube segmentation in optical coherence microscopy images.

Optical coherence microscopy (OCM) imaging of the Drosophila melanogaster (fruit fly) heart tube has enabled the non-invasive characterization of fly heart physiology in vivo. OCM generates large volumes of data, making it necessary to automate image analysis. Deep-learning-based neural network models have been developed to improve the efficiency of fly heart image segmentation. However, image artifacts caused by sample motion or reflections reduce the accuracy of the analysis. To improve the precision and efficiency of image data analysis, we developed an Attention LSTM U-Net model (FlyNet3.0), which incorporates an attention learning mechanism to track the beating fly heart in OCM images. The new model has improved the intersection over union (IOU) compared to FlyNet2.0 + with reflection artifacts from 86% to 89% and with movement from 81% to 89%. We also extended the capabilities of OCM analysis through the introduction of an automated, in vivo heart wall thickness measurement method, which has been validated on a Drosophila model of cardiac hypertrophy. This work will enable the comprehensive, non-invasive characterization of fly heart physiology in a high-throughput manner.

Characteristics and utility of high-resolution optical coherence microscopy images of endocervical canal lesions.

To investigate optical coherence microscopy (OCM) imaging features and the application value of these high-resolution images for identifying endocervical canal lesions (ECLs), which is a clinical dilemma in cervical cancer screening programs. In total, 520 OCM images were obtained by scanning the cervical canal lesions with an ultra-high-resolution OCM system (204 specimens from 73 patients). The OCM morphologic characteristics of ECLs were observed and summarized, and then 3 researchers performed a diagnostic test of OCM images of cervical canal lesions. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, 95% confidence interval of each parameter, and interinvestigator agreement (κ) were calculated. Normal endocervix, cysts, squamous metaplasia, high-grade squamous intraepithelial lesions involving glands, and invasive carcinoma had distinct OCM characteristics, which correlated well with corresponding H&E histologic sections. The accuracy, sensitivity, and specificity of the 3 researchers were 90.6%, 89.3% (95% CI, 86.5%-91.7%) and 91.6% (95% CI, 89.2%-93.5%), respectively. The positive predictive value was 90.1% (95% CI, 87.3%-92.4%), and the negative predictive value was 90.9% (95% CI, 88.5%-92.9%), with almost perfect agreement (κ = 0.874). The application of the OCM system in cervical canal lesions is feasible and could help improve detection of occult ECLs in cervical cancer screening programs. This study lays the foundation for further research on OCM in cervical canal lesions in vivo, which also has a potential impact on projecting pathologic evaluation beyond what is currently possible, perhaps globally.

Complex master-slave enhanced optical coherence microscopy

We present an instrument designed to facilitate localization and high-resolution, optical coherence microscopy (OCM) imaging of small biological samples immersed in a medium several orders of magnitude greater in volume. A modified turret-equipped microscope stand was inserted into the sample arm of a spectral domain optical coherence microscopy (SD-OCM) system. The instrument enabled swift change of imaging objectives through the incorporation of complex master-slave interferometry (CMSI), providing tolerance to dispersion for any objective through the acquisition of a few (≥2) calibration spectra. We demonstrate the instrument’s ability to localize and image samples by providing examples of its application to optical phantoms and to a porcine oocyte immersed in a biological culture medium.

P-233 Novel, non-invasive imaging techniques in quality assessment of mammalian eggs

Abstract Study question Can Optical Coherence Microscopy (OCM) imaging and Cytoplasmic Movement Velocity (CMV) analysis be useful in quality assessment of mature mammalian oocytes? Summary answer OCM allows for assessment of oocytes’ metaphase spindles, and CMV – the actomyosin cytoskeleton functionality, therefore they are potentially useful in oocyte selection for IVF. What is known already The success rate of IVF has significantly improved over the recent years, yet its efficiency plummets with the increasing age of female patients. Therefore, novel approaches to oocyte/embryo quality assessment are in high demand. Among the most promising new methods are OCM, a label-free, 3D tool for subcellular structure visualization, such as the meiotic spindle in mature oocytes or nuclear structure in immature oocytes, and the analysis of CMV, a time-lapse imaging-based method for studying properties of the actomyosin cytoskeleton, crucial for a plethora of cellular process. Both these methods provide unique insights into oocyte competence. Study design, size, duration MII spindle dimensions were measured in OCM-imaged freshly ovulated oocytes collected from young mice (FO-MII, n = 108) and correlated with the outcome of preimplantation development achieved after the oocytes’ activation. Spindles of FO-MII (n = 45) were also compared to spindles of oocytes collected from mice in advanced maternal age (AMA-MII, n = 44). CMV was compared in time-lapse imaged FO-MII (n = 24), postovulatory aged oocytes collected from young mice (PA-MII, n = 16), and AMA-MII (n = 19). Participants/materials, setting, methods Oocytes were collected from hormonally primed mice and imaged using a customized OCM protocol. They were then activated parthenogenetically and cultured for 5 days, followed by immunofluorescence staining to assess the total number of cells and number of cells in the first embryonic cell lineages. FO-MII, PA-MII, and AMA-MII were time-lapse imaged (6 frames per minute; 15 min). The images were subjected to particle image velocimetry analysis and the mean CMV was calculated. Main results and the role of chance Oocytes that achieved the blastocyst stage had smaller spindle volume (p < 0.001) and width (p < 0.05) as compared to those failing to do so. Univariate logistic regression analysis indicated that a 1 μm rise in the spindle width decreases the odds of blastocyst formation by 28% and the number of cells on average by 8, whereas a 100 μm3 rise in the volume – by almost 10% and 2.7 cells respectively. Moreover, the spindle volume is also related to the number of trophectodermal cells, and the spindle length - to the number of primitive endoderm cells in the blastocysts. Noteworthily, young oocytes’ spindles had significantly smaller volume (p < 0.0001), as well as were narrower (p < 0.0001) compared to their counterparts from aged females. A multivariate linear regression model combining spindle length with three morphokinetic parameters (t3, cc2b, tSB) explained 60% of the variability in the total number of cells in 5-day-old embryos (R2 = 0.604) while models based solely on a single morphokinetic parameters explained only up to approx. 54% (R2 = 0.537, for tSB). On the other hand, CMV analysis revealed that freshly ovulated eggs display faster cytoplasmic movements than those postovulatory-aged (p < 0.0001). Reduced cytoplasmic speed was also observed in oocytes collected from aged mice (p < 0.0001). Limitations, reasons for caution All studies presented here were performed on a mouse model, so the obtained data needs to be verified in the target species for validation of these methods as novel tools in oocyte selection protocols. Wider implications of the findings Our results suggest that OCM-based assessment of the MII spindle and short, 15-minute CMV imaging allow for the selection of higher quality oocytes and could potentially find application in assisted reproductive technologies, as they are non-invasive and may enhance the universally acclaimed oocyte/embryo quality assessment protocols. Trial registration number not applicable

Label-free metabolic and structural profiling of dynamic biological samples using multimodal optical microscopy with sensorless adaptive optics

Label-free optical microscopy has matured as a noninvasive tool for biological imaging; yet, it is criticized for its lack of specificity, slow acquisition and processing times, and weak and noisy optical signals that lead to inaccuracies in quantification. We introduce FOCALS (Fast Optical Coherence, Autofluorescence Lifetime imaging, and Second harmonic generation) microscopy capable of generating NAD(P)H fluorescence lifetime, second harmonic generation (SHG), and polarization-sensitive optical coherence microscopy (OCM) images simultaneously. Multimodal imaging generates quantitative metabolic and morphological profiles of biological samples in vitro, ex vivo, and in vivo. Fast analog detection of fluorescence lifetime and real-time processing on a graphical processing unit enables longitudinal imaging of biological dynamics. We detail the effect of optical aberrations on the accuracy of FLIM beyond the context of undistorting image features. To compensate for the sample-induced aberrations, we implemented a closed-loop single-shot sensorless adaptive optics solution, which uses computational adaptive optics of OCM for wavefront estimation within 2 s and improves the quality of quantitative fluorescence imaging in thick tissues. Multimodal imaging with complementary contrasts improves the specificity and enables multidimensional quantification of the optical signatures in vitro, ex vivo, and in vivo, fast acquisition and real-time processing improve imaging speed by 4–40 × while maintaining enough signal for quantitative nonlinear microscopy, and adaptive optics improves the overall versatility, which enable FOCALS microscopy to overcome the limits of traditional label-free imaging techniques.

Improved Diagnostic Imaging of Brain Tumors by Multimodal Microscopy and Deep Learning.

Fluorescence-guided surgery is a state-of-the-art approach for intraoperative imaging during neurosurgical removal of tumor tissue. While the visualization of high-grade gliomas is reliable, lower grade glioma often lack visible fluorescence signals. Here, we present a hybrid prototype combining visible light optical coherence microscopy (OCM) and high-resolution fluorescence imaging for assessment of brain tumor samples acquired by 5-aminolevulinic acid (5-ALA) fluorescence-guided surgery. OCM provides high-resolution information of the inherent tissue scattering and absorption properties of tissue. We here explore quantitative attenuation coefficients derived from volumetric OCM intensity data and quantitative high-resolution 5-ALA fluorescence as potential biomarkers for tissue malignancy including otherwise difficult-to-assess low-grade glioma. We validate our findings against the gold standard histology and use attenuation and fluorescence intensity measures to differentiate between tumor core, infiltrative zone and adjacent brain tissue. Using large field-of-view scans acquired by a near-infrared swept-source optical coherence tomography setup, we provide initial assessments of tumor heterogeneity. Finally, we use cross-sectional OCM images to train a convolutional neural network that discriminates tumor from non-tumor tissue with an accuracy of 97%. Collectively, the present hybrid approach offers potential to translate into an in vivo imaging setup for substantially improved intraoperative guidance of brain tumor surgeries.

Non-invasive red-light optogenetic control of Drosophila cardiac function

Drosophila is a powerful genetic model system for cardiovascular studies. Recently, optogenetic pacing tools have been developed to control Drosophila heart rhythm noninvasively with blue light, which has a limited penetration depth. Here we developed both a red-light sensitive opsin expressing Drosophila system and an integrated red-light stimulation and optical coherence microscopy (OCM) imaging system. We demonstrated noninvasive control of Drosophila cardiac rhythms using a single light source, including simulated tachycardia in ReaChR-expressing flies and bradycardia and cardiac arrest in halorhodopsin (NpHR)-expressing flies at multiple developmental stages. By using red excitation light, we were able to pace flies at higher efficiency and with lower power than with equivalent blue light excitation systems. The recovery dynamics after red-light stimulation of NpHR flies were observed and quantified. The combination of red-light stimulation, OCM imaging, and transgenic Drosophila systems provides a promising and easily manipulated research platform for noninvasive cardiac optogenetic studies.

Revealing brain pathologies with multimodal visible light optical coherence microscopy and fluorescence imaging.

.We present a multimodal visible light optical coherence microscopy (OCM) and fluorescence imaging (FI) setup. Specification and phantom measurements were performed to characterize the system. Two applications in neuroimaging were investigated. First, curcumin-stained brain slices of a mouse model of Alzheimer’s disease were examined. Amyloid-beta plaques were identified based on the fluorescence of curcumin, and coregistered morphological images of the brain tissue were provided by the OCM channel. Second, human brain tumor biopsies retrieved intraoperatively were imaged prior to conventional neuropathologic work-up. OCM revealed the three-dimensional structure of the brain parenchyma, and FI added the tumor tissue-specific contrast. Attenuation coefficients computed from the OCM data and the florescence intensity values were analyzed and showed a statistically significant difference for 5-aminolevulinic acid (5-ALA)-positive and -negative brain tissues. OCM findings correlated well with malignant hot spots within brain tumor biopsies upon histopathology. The combination of OCM and FI seems to be a promising optical imaging modality providing complementary contrast for applications in the field of neuroimaging.

Computer-Aided Diagnosis of Label-Free 3-D Optical Coherence Microscopy Images of Human Cervical Tissue.

Objective:Ultrahigh-resolution optical coherence microscopy (OCM) has recently demonstrated its potential for accurate diagnosis of human cervical diseases. One major challenge for clinical adoption, however, is the steep learning curve clinicians need to overcome to interpret OCM images. Developing an intelligent technique for computer-aided diagnosis (CADx) to accurately interpret OCM images will facilitate clinical adoption of the technology and improve patient care.Methods:497 high-resolution 3-D OCM volumes (600 cross-sectional images each) were collected from 159 ex vivo specimens of 92 female patients. OCM image features were extracted using a convolutional neural network (CNN) model, concatenated with patient information (e.g., age and HPV results), and classified using a support vector machine classifier. Ten-fold cross-validations were utilized to test the performance of the CADx method in a five-class classification task and a binary classification task.Results:An 88.3±4.9% classification accuracy was achieved for five fine-grained classes of cervical tissue, namely normal, ectropion, low-grade and high-grade squamous intraepithelial lesions (LSIL and HSIL), and cancer. In the binary classification task (low-risk [normal, ectropion and LSIL] vs. high-risk [HSIL and cancer]), the CADx method achieved an area-under-the-curve (AUC) value of 0.959 with an 86.7±11.4% sensitivity and 93.5±3.8% specificity.Conclusion:The proposed deep-learning based CADx method outperformed four human experts. It was also able to identify morphological characteristics in OCM images that were consistent with histopathological interpretations.Significance:Label-free OCM imaging, combined with deep-learning based CADx methods, hold a great promise to be used in clinical settings for the effective screening and diagnosis of cervical diseases.

Back Cover: Segmentation of Drosophila heart in optical coherence microscopy images using convolutional neural networks (J. Biophotonics 12/2018)

The cover, FlyNet, describes a convolutional neural network designed and trained to automatically segment the heart region of a fruit fly from time‐lapsed optical coherence microscopy (OCM) images. High‐resolution images of the beating fly heart were acquired using a non‐invasive OCM system. The FlyNet model accurately identifies and segments the heart from the images. Morphological and functional parameters can be analyzed to characterize the health status of the fly heart.Further details can be found in the article by Lian Duan, Xi Qin, Yuanhao He, et al. (e201800146) image

Low Cell-Matrix Adhesion Reveals Two Subtypes of Human Pluripotent Stem Cells

SummaryWe show that a human pluripotent stem cell (hPSC) population cultured on a low-adhesion substrate developed two hPSC subtypes with different colony morphologies: flat and domed. Notably, the dome-like cells showed higher active proliferation capacity and increased several pluripotent genes’ expression compared with the flat monolayer cells. We further demonstrated that cell-matrix adhesion mediates the interaction between cell morphology and expression of KLF4 and KLF5 through a serum response factor (SRF)-based regulatory double loop. Our results provide a mechanistic view on the coupling among adhesion, stem cell morphology, and pluripotency, shedding light on the critical role of cell-matrix adhesion in the induction and maintenance of hPSC.

Improved endoscopic optical coherence microscopy for imaging of humans airways in patients

Improved endoscopic optical coherence microscopy for imaging of humans airways in patients

Integrated Optical Coherence Tomography and Optical Coherence Microscopy Imaging of Ex Vivo Human Renal Tissues

We evaluated the feasibility of using optical coherence tomography and optical coherence microscopy technology to assess human kidney morphology. A total of 35 renal specimens from 19 patients, consisting of 12 normal tissues and 23 tumors (16 clear cell renal cell carcinomas, 5 papillary renal cell carcinomas and 2 oncocytomas) were imaged ex vivo after surgical resection. Optical coherence tomography and optical coherence microscopy images were compared to corresponding hematoxylin and eosin histology to identify characteristic features of normal and pathological renal tissues. Three pathologists blinded to histology evaluated the sensitivity and specificity of optical coherence microscopy images to differentiate normal from neoplastic renal tissues. Optical coherence tomography and optical coherence microscopy images of normal kidney revealed architectural features, including glomeruli, convoluted tubules, collecting tubules and loops of Henle. Each method of imaging renal tumors clearly demonstrated morphological changes and decreased imaging depth. Optical coherence tomography and microscopy features matched well with the corresponding histology. Three observers achieved 88%, 100% and 100% sensitivity, and 100%, 88% and 100% specificity, respectively, when evaluating normal vs neoplastic specimens using optical coherence microscopy images with substantial interobserver agreement (κ = 0.82, p <0.01). Integrated optical coherence tomography and optical coherence microscopy imaging provides coregistered, multiscale images of renal pathology in real time without exogenous contrast medium or histological processing. High sensitivity and specificity were achieved using optical coherence microscopy to differentiate normal from neoplastic renal tissues, suggesting possible applications for guiding renal mass biopsy or evaluating surgical margins.

Cellular resolution optical coherence microscopy with high acquisition speed for in-vivo human skin volumetric imaging

In this Letter, we report for the first time (to our knowledge) in-vivo volumetric optical coherence microscopy images of skin epidermal cells. We achieved micrometer-class resolution, 2 μm laterally and axially, with an acquisition speed of 23 K A-scans/s and over 90 dB sensitivity to a depth of 1 mm by employing a custom, liquid-lens-based, dynamic-focusing objective, a broadband light source, and a custom, astigmatism-corrected Czerny-Turner spectrometer with a high-speed complementary metal-oxide-semiconductor camera.

Cornea microstructure and mechanical responses measured with nonlinear optical and optical coherence microscopy using sub-10-fs pulses

A combined nonlinear optical microscopy (NLOM) and optical coherence microscopy (OCM) imaging system has been assembled in order to simultaneously capture co-registered volumetric images of corneal morphology and biochemistry. Tracking of cell nuclei visible in the OCM volume enabled the calculation of strain depth profile in response to changes in intraocular pressure for rabbit cornea stroma. Results revealed nonlinear responses with a depth dependent strain distribution, exhibiting smaller strains in the anterior and larger strains in the posterior stroma. Cross-sectional images of collagen lamellae, visible in NLOM, showed inhomogeneous collagen structure along the anterior-posterior direction that correlated well with the noted heterogeneous corneal mechanical responses.