Computational Model of DIC Microscopy for Reconstructing 3-D Specimens

2000 ◽  
Vol 39 (02) ◽  
pp. 105-109 ◽  
Author(s):  
F. Lanni ◽  
T. Kanade ◽  
F. Kagalwala

Abstract:Differential Interference Contrast (DIC) microscopy is a powerful visualization tool used to study live biological cells. Its use, however, has been limited to qualitative observations. The inherent non-linear relation between the object properties and the image intensity makes quantitative analysis difficult. As a first step towards measuring optical properties of objects from DIC images, we develop a model for the image formation process using methods consistent with energy conservation laws. We verify our model by comparing real image data of manufactured specimens to simulated images of virtual objects. As the next step, we plan to use this model to reconstruct the three-dimensional properties of unknown specimens.

RSC Advances ◽  
2020 ◽  
Vol 10 (50) ◽  
pp. 29868-29872
Author(s):  
Geun Wan Kim ◽  
Seokyoung Yoon ◽  
Jung Heon Lee ◽  
Ji Won Ha

Spherical AuNRs@mSiO2 have randomly oriented AuNR cores in 3D space, which could be resolved on the same focal plane by interference-based DIC microscopy.


2013 ◽  
Vol 19 (S4) ◽  
pp. 17-18
Author(s):  
E. Azevedo ◽  
M.F. Caeiro ◽  
M. Barata

Marine fungi occur either in Open Ocean or in the intertidal zone of sandy beaches, salt marshes and mangroves, where their hosts and substrates are found. The development of morphological adaptations like appendages and sheaths of the spores are vital to the settlement and attachment to substrate surfaces, floatation and dispersion on seawater. The morphological features of these appendages and sheaths of spores also have an important role in the identification of marine fungi. Differential interference contrast (DIC) microscopy is an essential tool for the observation of these mucilaginous structures in marine fungi spores and was therefore applied to marine mycota from surveys along the Portuguese coast.Since 1991 Portuguese marine fungi have been studied and characterized based on morphological identifications. The studied environments included salt marshes, sandy beaches and marinas. On these environments different substrates were collected such as plants and baits of Spartina maritima, different categories of drift substrates and Fagus sylvatica and Pinus pinaster baits. These substrates, which had been exposed to different conditions of permanent and temporary submersion, were subjected to an initial examination under the stereoscope microscope in order to detect fruit bodies and spores of marine fungi. These structures were then observed in order to achieve the taxonomic identifications of these fungi, based on dichotomous keys for marine fungi.To observe and characterize the wide variety of appendages and mucilaginous sheaths of the ascospores, basidiospores and conidia often invisible in the bright field of the light microscope, the use of DIC microscopy was implemented, because three-dimensional images are produced, highlighting them. The identified fungi were microphotographed with a Leica Wild MPS 52 with Fujichrome RTP- 135, 64T Tungsten.The studies carried out with substrates subjected to conditions of permanent submersion highlighted the dominance of Ascomycota with unitunicate asci. The unitunicate asci are thin - walled, persistent or early deliquescing, favoring ascospores release on marine environmental conditions (fig 1a, 1b and 1c). On the other hand, Ascomycota with bitunicate asci, were mainly detected on temporary submersion conditions. These fungi presented asci with active spore discharge and mucilaginous sheaths that contributed to substrate attachment (fig. 2a and 2b). Ascospores and conidia presented morphological diversity on their appendages, which has particular importance on their success in marine environment (figs. 3, 4, 5, and 6).Marine Mycologists are in agreement that the best technique to observe the appendages and sheaths of spores, often invisible in the bright-field microscope is the use of differential interference or phase contrast microscopy. DIC microscopy was then applied to the observation of Portuguese marine fungi enabling to thoroughly characterize the structures essential to their morphological identifications.


Author(s):  
Robert W. Mackin

This paper presents two advances towards the automated three-dimensional (3-D) analysis of thick and heavily-overlapped regions in cytological preparations such as cervical/vaginal smears. First, a high speed 3-D brightfield microscope has been developed, allowing the acquisition of image data at speeds approaching 30 optical slices per second. Second, algorithms have been developed to detect and segment nuclei in spite of the extremely high image variability and low contrast typical of such regions. The analysis of such regions is inherently a 3-D problem that cannot be solved reliably with conventional 2-D imaging and image analysis methods.High-Speed 3-D imaging of the specimen is accomplished by moving the specimen axially relative to the objective lens of a standard microscope (Zeiss) at a speed of 30 steps per second, where the stepsize is adjustable from 0.2 - 5μm. The specimen is mounted on a computer-controlled, piezoelectric microstage (Burleigh PZS-100, 68/μm displacement). At each step, an optical slice is acquired using a CCD camera (SONY XC-11/71 IP, Dalsa CA-D1-0256, and CA-D2-0512 have been used) connected to a 4-node array processor system based on the Intel i860 chip.


2021 ◽  
Vol 11 (13) ◽  
pp. 5931
Author(s):  
Ji’an You ◽  
Zhaozheng Hu ◽  
Chao Peng ◽  
Zhiqiang Wang

Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships’ postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate “Simulation–Real” ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images.


Author(s):  
P.G Young ◽  
T.B.H Beresford-West ◽  
S.R.L Coward ◽  
B Notarberardino ◽  
B Walker ◽  
...  

Image-based meshing is opening up exciting new possibilities for the application of computational continuum mechanics methods (finite-element and computational fluid dynamics) to a wide range of biomechanical and biomedical problems that were previously intractable owing to the difficulty in obtaining suitably realistic models. Innovative surface and volume mesh generation techniques have recently been developed, which convert three-dimensional imaging data, as obtained from magnetic resonance imaging, computed tomography, micro-CT and ultrasound, for example, directly into meshes suitable for use in physics-based simulations. These techniques have several key advantages, including the ability to robustly generate meshes for topologies of arbitrary complexity (such as bioscaffolds or composite micro-architectures) and with any number of constituent materials (multi-part modelling), providing meshes in which the geometric accuracy of mesh domains is only dependent on the image accuracy (image-based accuracy) and the ability for certain problems to model material inhomogeneity by assigning the properties based on image signal strength. Commonly used mesh generation techniques will be compared with the proposed enhanced volumetric marching cubes (EVoMaCs) approach and some issues specific to simulations based on three-dimensional image data will be discussed. A number of case studies will be presented to illustrate how these techniques can be used effectively across a wide range of problems from characterization of micro-scaffolds through to head impact modelling.


2003 ◽  
Vol 160 (5) ◽  
pp. 671-683 ◽  
Author(s):  
Alexey Khodjakov ◽  
Lily Copenagle ◽  
Michael B. Gordon ◽  
Duane A. Compton ◽  
Tarun M. Kapoor

Near-simultaneous three-dimensional fluorescence/differential interference contrast microscopy was used to follow the behavior of microtubules and chromosomes in living α-tubulin/GFP-expressing cells after inhibition of the mitotic kinesin Eg5 with monastrol. Kinetochore fibers (K-fibers) were frequently observed forming in association with chromosomes both during monastrol treatment and after monastrol removal. Surprisingly, these K-fibers were oriented away from, and not directly connected to, centrosomes and incorporated into the spindle by the sliding of their distal ends toward centrosomes via a NuMA-dependent mechanism. Similar preformed K-fibers were also observed during spindle formation in untreated cells. In addition, upon monastrol removal, centrosomes established a transient chromosome-free bipolar array whose orientation specified the axis along which chromosomes segregated. We propose that the capture and incorporation of preformed K-fibers complements the microtubule plus-end capture mechanism and contributes to spindle formation in vertebrates.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Katsumi Hagita ◽  
Takeshi Aoyagi ◽  
Yuto Abe ◽  
Shinya Genda ◽  
Takashi Honda

AbstractIn this study, deep learning (DL)-based estimation of the Flory–Huggins χ parameter of A-B diblock copolymers from two-dimensional cross-sectional images of three-dimensional (3D) phase-separated structures were investigated. 3D structures with random networks of phase-separated domains were generated from real-space self-consistent field simulations in the 25–40 χN range for chain lengths (N) of 20 and 40. To confirm that the prepared data can be discriminated using DL, image classification was performed using the VGG-16 network. We comprehensively investigated the performances of the learned networks in the regression problem. The generalization ability was evaluated from independent images with the unlearned χN. We found that, except for large χN values, the standard deviation values were approximately 0.1 and 0.5 for A-component fractions of 0.2 and 0.35, respectively. The images for larger χN values were more difficult to distinguish. In addition, the learning performances for the 4-class problem were comparable to those for the 8-class problem, except when the χN values were large. This information is useful for the analysis of real experimental image data, where the variation of samples is limited.


1992 ◽  
pp. 237-256 ◽  
Author(s):  
Zvi Kam ◽  
Hans Chen ◽  
John W. Sedat ◽  
David A. Agard

1984 ◽  
Vol 247 (3) ◽  
pp. E412-E419 ◽  
Author(s):  
L. S. Hibbard ◽  
R. A. Hawkins

Quantitative autoradiography is a powerful method for studying brain function by the determination of blood flow, glucose utilization, or transport of essential nutrients. Autoradiographic images contain vast amounts of potentially useful information, but conventional analyses can practically sample the data at only a small number of points arbitrarily chosen by the experimenter to represent discrete brain structures. To use image data more fully, computer methods for its acquisition, storage, quantitative analysis, and display are required. We have developed a system of computer programs that performs these tasks and has the following features: 1) editing and analysis of single images using interactive graphics, 2) an automatic image alignment algorithm that places images in register with one another using only the mathematical properties of the images themselves, 3) the calculation of mean images from equivalent images in different experimental serial image sets, 4) the calculation of difference images (e.g., experiment-minus-control) with the option to display only differences estimated to be statistically significant, and 5) the display of serial image metabolic maps reconstructed in three dimensions using a high-speed computer graphics system.


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