scholarly journals VALIS: Virtual Alignment of pathoLogy Image Series

2021 ◽  
Author(s):  
Chandler Dean Gatenbee ◽  
Ann-Marie Baker ◽  
Sandhya Prabhakaran ◽  
Robbert J.C. Slebos ◽  
Gunjan Mandal ◽  
...  
Keyword(s):  
Cell Segmentation ◽  
Spatial Analyses ◽  
Composite Image ◽  
Multiple Images ◽  
Image Series ◽  
Staining Methods ◽  
Automated Pipeline ◽  
Point Data ◽  
Pathology Image ◽  
Whole Slide Images

Spatial analyses can reveal important interactions between and among cells and their microenvironment. However, most existing staining methods are limited to a handful of markers per slice, thereby limiting the number of interactions that can be studied. This limitation is frequently overcome by registering multiple images to create a single composite image containing many markers. While there are several existing image registration methods for whole slide images (WSI), most have specific use cases. Here, we present the Virtual Alignment of pathoLogy Image Series (VALIS), a fully automated pipeline that opens, registers (rigid and/or non-rigid), and saves aligned slides in the ome.tiff format. VALIS has been tested with 273 immunohistochemistry (IHC) samples and 340 immunofluorescence (IF) samples, each of which contained between 2-69 images per sample. The registered WSI tend to have low error and are completed within a matter of minutes. In addition to registering slides, VALIS can also using the registration parameters to warp point data, such as cell centroids previously determined via cell segmentation and phenotyping. VALIS is written in Python and requires only few lines of code for execution. VALIS therefore provides a free, opensource, flexible, and simple pipeline for rigid and non-rigid registration of IF and/or IHC that can facilitate spatial analyses of WSI from novel and existing datasets.

Pathology image-based lung cancer subtyping using deeplearning features and cell-density maps

2020 ◽  
Vol 2020 (10) ◽  
pp. 64-1-64-5
Author(s):  
Mustafa I. Jaber ◽  
Christopher W. Szeto ◽  
Bing Song ◽  
Liudmila Beziaeva ◽  
Stephen C. Benz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Density ◽  
Majority Voting ◽  
Training Set ◽  
Density Maps ◽  
Color Deconvolution ◽  
Map Generation ◽  
Density Map ◽  
Pathology Image ◽  
Whole Slide Images

In this paper, we propose a patch-based system to classify non-small cell lung cancer (NSCLC) diagnostic whole slide images (WSIs) into two major histopathological subtypes: adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Classifying patients accurately is important for prognosis and therapy decisions. The proposed system was trained and tested on 876 subtyped NSCLC gigapixel-resolution diagnostic WSIs from 805 patients – 664 in the training set and 141 in the test set. The algorithm has modules for: 1) auto-generated tumor/non-tumor masking using a trained residual neural network (ResNet34), 2) cell-density map generation (based on color deconvolution, local drain segmentation, and watershed transformation), 3) patch-level feature extraction using a pre-trained ResNet34, 4) a tower of linear SVMs for different cell ranges, and 5) a majority voting module for aggregating subtype predictions in unseen testing WSIs. The proposed system was trained and tested on several WSI magnifications ranging from x4 to x40 with a best ROC AUC of 0.95 and an accuracy of 0.86 in test samples. This fully-automated histopathology subtyping method outperforms similar published state-of-the-art methods for diagnostic WSIs.

Generation of High Dynamic Range Image Based on Tone Composition of Multiple Images for Identical Scene

2010 ◽  
Vol 174 ◽  
pp. 132-135
Author(s):  
Lei Zhang ◽  
Yang Jin ◽  
Zhen Liu
Keyword(s):  
Distribution Curve ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Processing Method ◽  
Range Image ◽  
High Dynamic Range Image ◽  
Target Image ◽  
Composite Image ◽  
Multiple Images ◽  
High Dynamic

A High Dynamic Range Image (HDRI) processing method is described, which multiple images of identical scene but with different exposure amount can be processed. The processing method is: A composite distribution, which is as target distribution of HDRI, is derived by pixel counts accumulation of the multiple original images. The accumulated distribution curve of target HDRI is then computed. To generate a composite image as original, a weighting method is used and the gray levels of pixels are calculated based on pixels in the multiple images. The tone distribution and the accumulative distribution curve are also obtained. Finally, using the accumulative distribution curve of the target image and of the composite image, a tone-converting curve is derived to transform the composite image to HDRI. The result shows that this method can make better use of gradation information of the original images. The tone range and the contrast of target image are extended.

scholarly journals Geovisualization and Geographical Analysis for Fire Prevention

2020 ◽  
Vol 9 (6) ◽  
pp. 355
Author(s):  
Nicklas Guldåker
Keyword(s):  
Visual Analysis ◽  
Emergency Services ◽  
Risk Groups ◽  
Fire Prevention ◽  
Spatial Analyses ◽  
Strategic Decisions ◽  
Information Campaigns ◽  
Spatio Temporal ◽  
Point Data ◽  
Temporal Dimensions

Swedish emergency services still have relatively limited resources and time for proactive fire prevention. As a result of this, there is an extensive need for strategic working methods and knowledge to take advantage of spatial analyses. In addition, decision-making based on visualizations and analyses of their own collected data has the potential to increase the validity of strategic decisions. The objective of this paper is to critically examine how some different geovisualization techniques—point data, kernel density and choropleth mapping—actively can complement each other and be applied in fire preventive work. The results show that each technique itself has limitations, but that, in combination, they increase the scope for interpretation and the possibilities of targeting different forms of preventive measures. The investigated geovisualization techniques facilitate various forms of fire prevention such as identifying which areas to prioritize for outreach, home visits, identification and targeting of different risk groups and customized information campaigns about certain types of fires in risk-prone areas. Furthermore, fairly simple mapping techniques can be utilized directly to evaluate incident reports and increase the quality of geocoded fire incidents. The study also shows how some of these techniques can be applied when analyzing residential fire incidents and their relation to underlying structural and socio-economic factors as well as spatio-temporal dimensions of fire incident data. The spatial analyses and supporting maps can help find and predict risk areas for residential fires or be used directly to formulate hypotheses on fire patterns. The generic functionality of the visualization methods makes them also useful for visual analysis of other types of incidents, such as reported crimes and accidents. Finally, the results are applicable to a work process adapted to the Swedish legislation on confidential data.

scholarly journals Pathology Image Exchange: The Dutch Digital Pathology Platform for Exchange of Whole-Slide Images for Efficient Teleconsultation, Telerevision, and Virtual Expert Panels

2019 ◽  
pp. 1-7 ◽  
Author(s):  
Paul J. van Diest ◽  
André Huisman ◽  
Jaap van Ekris ◽  
Jos Meijer ◽  
Stefan Willems ◽  
...  
Keyword(s):  
Virtual Slide ◽  
Digital Pathology ◽  
Real Life ◽  
Patient Privacy ◽  
Remote Consultation ◽  
Technical Validation ◽  
Virtual Expert ◽  
The Many ◽  
Pathology Image ◽  
Whole Slide Images

Among the many uses of digital pathology, remote consultation, remote revision, and virtual slide panels may be the most important ones. This requires basic slide scanner infrastructure in participating laboratories to produce whole-slide images. More importantly, a software platform is needed for exchange of these images and functionality to support the processes around discussing and reporting on these images without breaching patient privacy. This poses high demands on the setup of such a platform, given the inherent complexity of the handling of digital pathology images. In this article, we describe the setup and validation of the Pathology Image Exchange project, which aimed to create a vendor-independent platform for exchange of whole-slide images between Dutch pathology laboratories to facilitate efficient teleconsultation, telerevision, and virtual slide panels. Pathology Image Exchange was released in April 2018 after technical validation, and a first successful validation in real life has been performed for hematopathology cases.

scholarly journals TIAToolbox: An End-to-End Toolbox for Advanced Tissue Image Analytics

2021 ◽  
Author(s):  
Johnathan Pocock ◽  
Simon Graham ◽  
Quoc Dang Vu ◽  
Mostafa Jahanifar ◽  
Srijay Deshpande ◽  
...  
Keyword(s):  
Best Practices ◽  
Open Source Software ◽  
State Of The Art ◽  
Seamless Integration ◽  
Sheer Size ◽  
End To End ◽  
Computational Pathology ◽  
User Friendly ◽  
Pathology Image ◽  
Whole Slide Images

Computational Pathology (CPath) has seen rapid growth in recent years, driven by advanced deep learning (DL) algorithms. These algorithms typically share the same sequence of steps. However, due to the sheer size and complexity of handling large multi-gigapixel whole-slide images, there is no open-source software library that provides a generic end-to-end API for pathology image analysis using best practices for CPath. Most researchers have designed custom pipelines from the bottom-up, restricting the development of advanced CPath algorithms to specialist users. To help overcome this bottleneck, we present TIAToolbox, a Python toolbox designed to make CPath more accessible to new and advanced CPath scientists and pathologists alike. We provide a usable and adaptable library with efficient, cutting-edge and unit-tested tools for data loading, pre-processing, model inference, post-processing and visualization. This enables all kinds of users to easily build upon recent DL developments in the CPath literature. TIAToolbox provides a user-friendly modular API to enable seamless integration of advanced DL algorithms. We show with the help of examples how state-of-the-art DL algorithms can be streamlined using TIAToolbox.

Uranyl Acetate and Rotary Shadowing to Increase the Contrast of Small Aggregated Virus Particles

1969 ◽  
Vol 27 ◽  
pp. 424-425
Author(s):  
Lee F. Ellis ◽  
Richard M. Van Frank ◽  
Walter J. Kleinschmidt
Keyword(s):  
Electron Microscopy ◽  
Tissue Culture ◽  
Density Gradient ◽  
Density Gradient Centrifugation ◽  
Gradient Centrifugation ◽  
Uranyl Acetate ◽  
Interferon Inducer ◽  
Virus Particles ◽  
Staining Methods ◽  
Rotary Shadowing

The extract from Penicillum stoliniferum, known as statolon, has been purified by density gradient centrifugation. These centrifuge fractions contained virus particles that are an interferon inducer in mice or in tissue culture. Highly purified preparations of these particles are difficult to enumerate by electron microscopy because of aggregation. Therefore a study of staining methods was undertaken.

Advances in imaging SIMS studies of stained and BrdU-labelled human metaphase chromosomes

1995 ◽  
Vol 53 ◽  
pp. 932-933
Author(s):  
R. Levi-Setti ◽  
J. M. Chabala ◽  
R. Espinosa ◽  
M. M. Le Beau
Keyword(s):  
High Performance ◽  
Secondary Ion Mass Spectrometry ◽  
Analytical Sensitivity ◽  
Metaphase Chromosomes ◽  
Banding Patterns ◽  
Sector Mass Spectrometer ◽  
Staining Methods ◽  
The University ◽  
Secondary Ion ◽  
Better Than

We have shown previously that isotope-labelled nucleotides in human metaphase chromosomes can be detected and mapped by imaging secondary ion mass spectrometry (SIMS), using the University of Chicago high resolution scanning ion microprobe (UC SIM). These early studies, conducted with BrdU- and 14C-thymidine-labelled chromosomes via detection of the Br and 28CN- (14C14N-> labelcarrying signals, provided some evidence for the condensation of the label into banding patterns along the chromatids (SIMS bands) reminiscent of the well known Q- and G-bands obtained by conventional staining methods for optical microscopy. The potential of this technique has been greatly enhanced by the recent upgrade of the UC SIM, now coupled to a high performance magnetic sector mass spectrometer in lieu of the previous RF quadrupole mass filter. The high transmission of the new spectrometer improves the SIMS analytical sensitivity of the microprobe better than a hundredfold, overcoming most of the previous imaging limitations resulting from low count statistics.

Stereo modeling of HVEM specimens by serial tilting and computer graphics

1986 ◽  
Vol 44 ◽  
pp. 34-35
Author(s):  
J.R. McIntosh ◽  
D.L. Stemple ◽  
William Bishop ◽  
G.W. Hannaway
Keyword(s):  
Computer Graphics ◽  
Analytical Methods ◽  
Photographic Film ◽  
Complex Structures ◽  
Multiple Objects ◽  
Multiple Images ◽  
3 Dimensional

EM specimens often contain 3-dimensional information that is lost during micrography on a single photographic film. Two images of one specimen at appropriate orientations give a stereo view, but complex structures composed of multiple objects of graded density that superimpose in each projection are often difficult to decipher in stereo. Several analytical methods for 3-D reconstruction from multiple images of a serially tilted specimen are available, but they are all time-consuming and computationally intense.

A New Detector System For The HB5 Stem Instrument

1985 ◽  
Vol 43 ◽  
pp. 134-135
Author(s):  
J.M. Cowley
Keyword(s):  
Dark Field ◽  
Detector System ◽  
Electron Holography ◽  
Small Specimen ◽  
Bright Field ◽  
Multiple Images ◽  
Practical Applications ◽  
Wide Range ◽  
Diffraction Patterns ◽  
Operational Modes

The HB5 STEM instrument at ASU has been modified previously to include an efficient two-dimensional detector incorporating an optical analyser device and also a digital system for the recording of multiple images. The detector system was built to explore a wide range of possibilities including in-line electron holography, the observation and recording of diffraction patterns from very small specimen regions (having diameters as small as 3Å) and the formation of both bright field and dark field images by detection of various portions of the diffraction pattern. Experience in the use of this system has shown that sane of its capabilities are unique and valuable. For other purposes it appears that, while the principles of the operational modes may be verified, the practical applications are limited by the details of the initial design.

Energy-filtered imaging of polymer microstructure

1996 ◽  
Vol 54 ◽  
pp. 162-163
Author(s):  
K. Siangchaew ◽  
J. Bentley ◽  
M. Libera
Keyword(s):  
Energy Loss ◽  
Heavy Element ◽  
Spectroscopic Imaging ◽  
Data Set ◽  
Polymer Microstructure ◽  
Entire Energy ◽  
Staining Methods ◽  
Spectrum Imaging ◽  
Resolution Data

Energy-filtered electron-spectroscopic TEM imaging provides a new way to study the microstructure of polymers without heavy-element stains. Since spectroscopic imaging exploits the signal generated directly by the electron-specimen interaction, it can produce richer and higher resolution data than possible with most staining methods. There are basically two ways to collect filtered images (fig. 1). Spectrum imaging uses a focused probe that is digitally rastered across a specimen with an entire energy-loss spectrum collected at each x-y pixel to produce a 3-D data set. Alternatively, filtering schemes such as the Zeiss Omega filter and the Gatan Imaging Filter (GIF) acquire individual 2-D images with electrons of a defined range of energy loss (δE) that typically is 5-20 eV.

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