Quantitation In Immunohistochemistry

Valuable information concerning the relative amounts of the end proucts of histochemical and immunochemical reactions present in sections may be provided by qualitative evaluations, however, greater reliance is often placed on quantitative evaluations. Many quantitative evaluations are based on the use of image analysis and optical density readings of the visible end products. An important question is whether these quantitative measurements are reliable, accurate and reproducible, and if quantitation of these reactions offers any real advantage over qualitative evaluations.

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ME-VAE: Multi-Encoder Variational AutoEncoder for Controlling Multiple Transformational Features in Single Cell Image Analysis

10.1101/2021.04.22.441005 ◽

2021 ◽

Author(s):

Luke Ternes ◽

Mark Dane ◽

Marilyne Labrie ◽

Gordon Mills ◽

Joe Gray ◽

...

Keyword(s):

Image Analysis ◽

Single Cell ◽

Imaging Features ◽

Phenotypic Differences ◽

Cell Image ◽

Intensity Measurements ◽

Quantitative Measurements ◽

Variational Autoencoder ◽

Cell Image Analysis ◽

Organizational Features

AbstractImage-based cell phenotyping relies on quantitative measurements as encoded representations of cells; however, defining suitable representations that capture complex imaging features is challenging since there are many obstacles, including segmentation and identifying subcellular compartments for feature extraction. Variational autoencoder (VAE) approaches produce encouraging results by mapping from an image to a representative descriptor, and outperform classical hand-crafted features for morphology, intensity, and texture at differentiating data. Although VAEs show promising results for capturing morphological and organizational features in tissue, single cell image analyses based on VAEs often fail to identify biologically informative features due to the intrinsic amount of uninformative variability. Herein, we propose a multi-encoder VAE (ME-VAE) in single cell image analysis using transformed images as a self-supervised signal to extract transform-invariant biologically meaningful features. We show that the proposed architecture improves analysis by making distinct populations more separable compared to traditional VAEs and intensity measurements by enhancing phenotypic differences between cells and by improving correlations to other modalities.

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Relative optical density image analysis of digitized radiograms for tibial fracture healing monitoring

International Congress Series ◽

10.1016/j.ics.2004.03.277 ◽

2004 ◽

Vol 1268 ◽

pp. 1292

Author(s):

W. Glinkowski ◽

M. Kornacki ◽

M. Ambroziak ◽

A. Górecki

Keyword(s):

Image Analysis ◽

Fracture Healing ◽

Optical Density ◽

Tibial Fracture ◽

Relative Optical Density ◽

Density Image

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Automated quantitative measurements and associated error covariances for planetary image analysis

Advances in Space Research ◽

10.1016/j.asr.2015.03.043 ◽

2015 ◽

Vol 56 (1) ◽

pp. 92-105 ◽

Cited By ~ 5

Author(s):

P.D. Tar ◽

N.A. Thacker ◽

J.D. Gilmour ◽

M.A. Jones

Keyword(s):

Image Analysis ◽

Quantitative Measurements

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Image analysis: From pixels to data

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100148514 ◽

1993 ◽

Vol 51 ◽

pp. 536-537

Author(s):

John C. Russ

Keyword(s):

Image Analysis ◽

3D Visualization ◽

Two Dimensional ◽

Multiple Images ◽

Quantitative Measurements ◽

Ready Availability ◽

Computer Based ◽

Processing Measurement ◽

Numeric Information ◽

The Right

This meeting is about microscopy, which deals with images, and this special session is specifically concerned with the methods for interpretation of those images. It should not be necessary to preach to such an audience about the need for appropriate tools for performing quantitative measurements to extract numeric information from images. Most of these tools are based on the ready availability of computers and peripherals which allow the acquisition, storage, processing, measurement and interpretation of the data. Provided we can also teach the scientist to ask the right questions and correctly use the results, image analysis can offer a powerful tool for the understanding of structure. Since most of this structure is threedimensional, whereas images are essentially two-dimensional, there is need for stereological interpretation, and for the construction of 3D visualization from multiple images. These are dealt with in subsequent papers in this session. This talk is primarily an introduction to the basics of computer-based image analysis - the tools for obtaining information from images, or “data from pixels.”

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Nuclear size, shape, and density in endometrial carcinoma

International Journal of Gynecological Cancer ◽

10.1136/ijgc-00009577-200401000-00019 ◽

2004 ◽

Vol 14 (1) ◽

pp. 138-144

Author(s):

J. Miller ◽

J. P. Geisler ◽

K. J. Manahan ◽

H. E. Geisler ◽

G. A. Miller ◽

...

Keyword(s):

Cell Cycle ◽

Image Analysis ◽

Endometrial Carcinoma ◽

Optical Density ◽

Nuclear Size ◽

Prognostic Indicators ◽

Depth Of Invasion ◽

Prognostic Features ◽

Entire Cell

ObjectiveThe authors, using image analysis, previously demonstrated nuclear size and summed optical density to be independent prognostic indicators of recurrence in patients with endometrial carcinoma. The same tumors were analyzed by studying the optical features in the G0–G1 peak to see if this changed the values found as well as their importance as prognostic features at greater than 5 years of follow-up.MethodsTumors from 74 consecutive patients, surgically treated, with endometrial cancer, were evaluated. Survival, depth of invasion, lymphvascular space invasion, FIGO stage, grade, histology were analyzed. DNA index, progesterone receptor status, as well as nuclear size (NUSZ), shape (NUSH), and summed optical density (NUSD) were evaluated. NUSZ, NUSH, and NUSD were quantified using image analysis.ResultsFifteen patients died from disease during the observation period of the study. Mean follow-up was 82 months with a median of 84 months. Forty-nine patients had stage I cancers, five stage II, 17 stage III, and three stage IV. NUSZ and NUSD were all significantly different between the original (entire cell cycle) and the re-measured (G0G1 only) values (both P < 0.001). Multivariate analysis showed both the original (P = 0.0001) and G0G1-only (P = 0.046) NUSZ and the original (P = 0.0002) and G0G1-only (P = 0.018) NUSD to be independent prognosticators of survival.ConclusionImage analysis is able to quantify cellular and nuclear parameters not otherwise quantifiable. NUSD and NUSZ correlated with traditional prognostic indicators, were demonstrated independent predictors of survival at over 5 years of follow-up. Although the re-measured NUSZ and NUSD from only the G0–G1 peak were significantly different from the original NUSZ and NUSD, they were not as valuable as prognostic factors. Nuclear size and summed optical density measured from the entire cell cycle are independent prognostic indicators of survival at greater than 5 years of follow-up. Measuring nuclear morphometric features in the G0–G1 peak only does not add any new prognostic information.

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A Technique for Section Thickness Evaluation for Microphotometry and Image Analysis of Sectioned Nuclei

Analytical Cellular Pathology ◽

10.1155/1998/607373 ◽

1998 ◽

Vol 17 (2) ◽

pp. 125-130 ◽

Cited By ~ 8

Author(s):

R. L. Cabrini ◽

A. Folco ◽

S. Orrea ◽

M. T. Savino ◽

A. M. Schwint ◽

...

Keyword(s):

Image Analysis ◽

Optical Density ◽

Section Thickness ◽

Standard Material ◽

Tissue Sections ◽

Advantages And Disadvantages ◽

Paraffin Block ◽

Dna Measurements ◽

Different Thickness

The exact knowledge of the section thickness is a requisite for making the necessary corrections on DNA measurements in tissue sections. Several methods have been proposed to evaluate section thickness, each of them with advantages and disadvantages depending on the type of specimen and equipment available. We herein report another method based on preparation of standard material whose optical density varies as a function of its thickness and is sectioned and measured alongside the tissue specimen. The standards consist of celloidin cylinders stained with the PAS reaction and embedded in paraffin. For prior characterization of the cylinders, sections of different thickness were obtained and mounted. The optical density of each section was measured by direct microphotometry or image analysis. The actual thickness of each section was evaluated following re-embedding of piled groups of sections in a paraffin block and transversal sectioning. The thickness was then measured with a micrometric eye-piece. Optical density and actual thickness of each section were plotted on a normogram curve. Once a given tissue is sectioned alongside with the reference cylinder, the actual thickness is determined by its optical density on the normogram curve.

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Fibre orientation in short-fibre-reinforced thermoplastics II. Quantitative measurements by image analysis

Composites Science and Technology ◽

10.1016/s0266-3538(98)00101-8 ◽

1999 ◽

Vol 59 (4) ◽

pp. 547-560 ◽

Cited By ~ 52

Author(s):

B Mlekusch

Keyword(s):

Image Analysis ◽

Fibre Orientation ◽

Short Fibre ◽

Quantitative Measurements ◽

Reinforced Thermoplastics

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Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning

AJP Renal Physiology ◽

10.1152/ajprenal.00629.2017 ◽

2018 ◽

Vol 315 (6) ◽

pp. F1644-F1651 ◽

Cited By ~ 6

Author(s):

Susan M. Sheehan ◽

Ron Korstanje

Keyword(s):

Machine Learning ◽

Image Analysis ◽

Statistical Analysis ◽

High Throughput ◽

Quantitative Data ◽

Human Interaction ◽

Physiological Data ◽

Data Sets ◽

Quantitative Measurements ◽

Consistent Manner

Current methods of scoring histological kidney samples, specifically glomeruli, do not allow for collection of quantitative data in a high-throughput and consistent manner. Neither untrained individuals nor computers are presently capable of identifying glomerular features, so expert pathologists must do the identification and score using a categorical matrix, complicating statistical analysis. Critical information regarding overall health and physiology is encoded in these samples. Rapid comprehensive histological scoring could be used, in combination with other physiological measures, to significantly advance renal research. Therefore, we used machine learning to develop a high-throughput method to automatically identify and collect quantitative data from glomeruli. Our method requires minimal human interaction between steps and provides quantifiable data independent of user bias. The method uses free existing software and is usable without extensive image analysis training. Validation of the classifier and feature scores in mice is highlighted in this work and shows the power of applying this method in murine research. Preliminary results indicate that the method can be applied to data sets from different species after training on relevant data, allowing for fast glomerular identification and quantitative measurements of glomerular features. Validation of the classifier and feature scores are highlighted in this work and show the power of applying this method. The resulting data are free from user bias. Continuous data, such that statistical analysis can be performed, allows for more precise and comprehensive interrogation of samples. These data can then be combined with other physiological data to broaden our overall understanding of renal function.

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