scholarly journals Structured Illumination-Based Super-Resolution Optical Microscopy for Hemato- and Cyto-Pathology Applications

2013 ◽  
Vol 36 (1-2) ◽  
pp. 27-35 ◽  
Author(s):  
Tieqiao Zhang ◽  
Samantha Osborn ◽  
Chloe Brandow ◽  
Denis Dwyre ◽  
Ralph Green ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Optical Microscopy ◽  
Blood Cells ◽  
Super Resolution ◽  
Structured Illumination ◽  
Tissue Sections ◽  
Moiré Effect ◽  
Different Types ◽  
Microscopy Images ◽  
Conventional Light Microscopy

Structured illumination fluorescence microscopy utilizes interfering light and the moiré effect to enhance spatial resolution to about a half of that of conventional light microscopy, i.e. approximately 90 nm. In addition to the enhancement in thexandydirections, it also allows enhancement of resolution in thez- direction by the same factor of two (to approximately 220 nm), making it a powerful tool for 3-D morphology studies of fluorescently labeled cells or thin tissue sections. In this report, we applied this technique to several types of blood cells that are commonly seen in hematopathology. Compared with standard brightfield and ordinary fluorescence microscopy images, the 3-D morphology results clearly reveal the morphological features of different types of normal blood cells. We have also used this technique to evaluate morphologies of abnormal erythrocytes and compare them with those recorded on normal cells. The results give a very intuitive presentation of morphological structures of erythrocytes with great details. This research illustrates the potential of this technique to be used in hematology and cyto-pathology studies aimed at identifying nanometer-sized features that cannot be distinguished otherwise with conventional optical microscopy.

scholarly journals CryoSIM: super-resolution 3D structured illumination cryogenic fluorescence microscopy for correlated ultrastructural imaging

Optica ◽  
2020 ◽  
Vol 7 (7) ◽  
pp. 802 ◽  
Author(s):  
Michael A. Phillips ◽  
Maria Harkiolaki ◽  
David Miguel Susano Pinto ◽  
Richard M. Parton ◽  
Ana Palanca ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Super Resolution ◽  
Structured Illumination

scholarly journals Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images

2021 ◽  
Author(s):  
ZAFRAN HUSSAIN SHAH ◽  
Marcel Müller ◽  
TUNG-CHENG WANG ◽  
Philip Scheidig ◽  
Axel Schneider ◽  
...  
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Microscopy Images

scholarly journals Visualizing ultrastructural details of placental tissue with super-resolution structured illumination microscopy

2020 ◽  
Author(s):  
Luis E. Villegas-Hernández ◽  
Mona Nystad ◽  
Florian Ströhl ◽  
Purusotam Basnet ◽  
Ganesh Acharya ◽  
...  
Keyword(s):  
Cell Biology ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Placental Tissue ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Chorionic Villi ◽  
Tissue Sections ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded

AbstractSuper-resolution fluorescence microscopy is a widely employed technique in cell biology research, yet remains relatively unexplored in the field of histo-pathology. Here, we describe the sample preparation steps and acquisition parameters necessary to obtain fluorescent multicolor super-resolution structured illumination microscopy (SIM) images of both formalin-fixed paraffin-embedded and cryo-preserved placental tissue sections. We compare super-resolved images of chorionic villi against diffraction-limited deconvolution microscopy and demonstrate the significant contrast and resolution enhancement attainable with SIM. We show that SIM resolves ultrastructural details such as the syncytiotrophoblast’s microvilli brush border, which up until now has been only resolvable by electron microscopy.

scholarly journals Resolving the internal morphology of core–shell microgels with super-resolution fluorescence microscopy

2020 ◽  
Vol 2 (1) ◽  
pp. 323-331 ◽  
Author(s):  
Pia Otto ◽  
Stephan Bergmann ◽  
Alice Sandmeyer ◽  
Maxim Dirksen ◽  
Oliver Wrede ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Internal Structure ◽  
Single Molecule ◽  
Fluorescent Dyes ◽  
Super Resolution ◽  
Core Shell ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Internal Morphology

We investigate the internal structure of smart core–shell microgels by super-resolution fluorescence microscopy by combining of 3D single molecule localization and structured illumination microscopy using freely diffusing fluorescent dyes.

scholarly journals Improving axial resolution in Structured Illumination Microscopy using deep learning

2021 ◽  
Vol 379 (2199) ◽  
Author(s):  
Miguel A. Boland ◽  
Edward A. K. Cohen ◽  
Seth R. Flaxman ◽  
Mark A. A. Neil
Keyword(s):  
Deep Learning ◽  
Optical Microscopy ◽  
Super Resolution ◽  
Structured Illumination ◽  
Learning Models ◽  
Axial Resolution ◽  
Structured Illumination Microscopy ◽  
Recent Advances ◽  
Robust To Noise ◽  
Up Scaling

Structured Illumination Microscopy (SIM) is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy. Using recent advances in image up-scaling through deep learning models, we demonstrate a method to reconstruct 3D SIM image stacks with twice the axial resolution attainable through conventional SIM reconstructions. We further demonstrate our method is robust to noise and evaluate it against two-point cases and axial gratings. Finally, we discuss potential adaptions of the method to further improve resolution. This article is part of the Theo Murphy meeting issue ‘Super-resolution structured illumination microscopy (part 1)’.

scholarly journals Application of Convolutional Neural Networks Towards Nuclei Segmentation in Localization-based Super-Resolution Fluorescence Microscopy Images

2021 ◽  
Author(s):  
Christopher Mela ◽  
Yang Liu
Keyword(s):  
Neural Networks ◽  
Fluorescence Microscopy ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Biological Research ◽  
Nuclei Segmentation ◽  
Segmentation Accuracy ◽  
Conventional Fluorescence Microscopy ◽  
Microscopy Images ◽  
Resolution Data

Abstract Background Automated segmentation of nuclei in microscopic images has been conducted to enhance throughput in pathological diagnostics and biological research. Segmentation accuracy and speed has been significantly enhanced with the advent of convolutional neural networks. A barrier in the broad application of neural networks to nuclei segmentation is the necessity to train the network using a set of application specific images and image labels. Previous works have attempted to create broadly trained networks for universal nuclei segmentation; however, such networks do not work on all imaging modalities, and best results are still commonly found when the network is retrained on user specific data. Stochastic optical reconstruction microscopy (STORM) based super-resolution fluorescence microscopy has opened a new avenue to image nuclear architecture at nanoscale resolutions. Due to the large size and discontinuous features typical of super-resolution images, automatic nuclei segmentation can be difficult. In this study, we apply commonly used networks (Mask R-CNN and UNet architectures) towards the task of segmenting super-resolution images of nuclei. First, we assess whether networks broadly trained on conventional fluorescence microscopy datasets can accurately segment super-resolution images. Then, we compare the resultant segmentations with results obtained using networks trained directly on our super-resolution data. We next attempt to optimize and compare segmentation accuracy using three different neural network architectures. Results Results indicate that super-resolution images are not broadly compatible with neural networks trained on conventional bright-field or fluorescence microscopy images. When the networks were trained on super-resolution data, however, we attained nuclei segmentation accuracies (F1-Score) in excess of 0.8, comparable to past results found when conducting nuclei segmentation on conventional fluorescence microscopy images. Overall, we achieved the best results utilizing the Mask R-CNN architecture. Conclusions We found that convolutional neural networks are powerful tools capable of accurately and quickly segmenting localization-based super-resolution microscopy images of nuclei. While broadly trained and widely applicable segmentation algorithms are desirable for quick use with minimal input, optimal results are still found when the network is both trained and tested on visually similar images. We provide a set of Colab notebooks to disseminate the software into the broad scientific community (https://github.com/YangLiuLab/Super-Resolution-Nuclei-Segmentation).

Super-resolution structured illumination fluorescence microscopy of the lateral wall of the cochlea: the Connexin26/30 proteins are separately expressed in man

2016 ◽  
Vol 365 (1) ◽  
pp. 13-27 ◽  
Author(s):  
Wei Liu ◽  
Fredrik Edin ◽  
Hans Blom ◽  
Peetra Magnusson ◽  
Annelies Schrott-Fischer ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Super Resolution ◽  
Lateral Wall ◽  
Structured Illumination

MEASURING SHAPE AND MOTION OF WHITE BLOOD CELLS FROM SEQUENCES OF FLUORESCENCE MICROSCOPY IMAGES

1995 ◽  
pp. 305-316 ◽  
Author(s):  
Luren yang ◽  
Fritz Albregtsen ◽  
Tor Lønnestad ◽  
Per Grøttum ◽  
Jens-Gustav Iversen ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Blood Cells ◽  
White Blood Cells ◽  
Microscopy Images

scholarly journals Structured illumination microscopy and its new developments

2016 ◽  
Vol 09 (03) ◽  
pp. 1630010 ◽  
Author(s):  
Jianling Chen ◽  
Caimin Qiu ◽  
Minghai You ◽  
Xiaogang Chen ◽  
Hongqin Yang ◽  
...  
Keyword(s):  
Optical Microscopy ◽  
Spatial Resolution ◽  
Imaging Techniques ◽  
Super Resolution ◽  
Living Cells ◽  
The Other ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Recent Developments ◽  
Super Resolution Microscopy

Optical microscopy allows us to observe the biological structures and processes within living cells. However, the spatial resolution of the optical microscopy is limited to about half of the wavelength by the light diffraction. Structured illumination microscopy (SIM), a type of new emerging super-resolution microscopy, doubles the spatial resolution by illuminating the specimen with a patterned light, and the sample and light source requirements of SIM are not as strict as the other super-resolution microscopy. In addition, SIM is easier to combine with the other imaging techniques to improve their imaging resolution, leading to the developments of diverse types of SIM. SIM has great potential to meet the various requirements of living cells imaging. Here, we review the recent developments of SIM and its combination with other imaging techniques.

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