ImageJ | EPFL Graph Search

ImageJ est un logiciel multiplate-forme, libre et open source de traitement et d's développé par les National Institutes of Health, en 1997. Il est écrit en Java et permet l'ajout de fonctionnalités via des plugins et macros. ImageJ est exécuté comme un applet téléchargeable sur tout ordinateur disposant d'une machine virtuelle Java 5 ou ultérieure. Des distributions pour Microsoft Windows, Mac OS, Mac OS X, Linux, et Zaurus sont disponibles en téléchargement. Le développeur du projet, Wayne Rasband, fait partie du Research Services Branch du National Institute of Mental Health. Caractéristiques ImageJ est un logiciel libre : le code source est en accès libre et peut être modifié. C'est un logiciel multiplateforme, en raison de son fonctionnement sur une machine virtuelle Java. ImageJ peut visualiser, éditer, traiter, et analyser les images ou des piles d'images (stack) en 8-bits, 16-bits, 32-bits et les images couleur. ImageJ est capable d'ouvrir et sauver des images d

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features

Cathrin Brisken, Olivier Burri, Fabio De Martino, Quentin Juppet, Daniel Sage, Michaël Unser, Martin Weigert

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cell contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E- and DAPI-stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier.

2021

Analytical Methods, Correlative Microscopy and Software Tools for Quantitative Single Molecule Localization Microscopy

Arun Shivanandan

Single Molecule Localization Microscopy (SMLM) techniques, such as PhotoActivated Localization Microscopy (PALM) and STochastic Optical Reconstruction Microscopy(STORM), can get around the diffraction limited resolution of conventional fluorescent microscopy (FM). However, there are a number of possible sources of imaging errors in SMLM which can significantly impact such studies. They include labeling artifacts, a limited detection efficiency of label molecules(of about 40-60% in PALM, e.g.,) and an uncertainty in localization in the range of 20-50nm. This thesis describes a rigorous review of such sources of error. Also, the SMLM readout is different from FM, the tools used in FM may not be directly applicable to SMLM. Accurate and precise quantitative imaging with SMLM requires analytical, experimental and software tools that address such issues. We describe analytical methods that accounts for two major sources of errors in analysis of membrane protein organization with SMLM. We model limited detection efficiency as independent subsampling of the set of label molecules. We then use a theoretical property of commonly used second order properties in quantitative SMLM, such as Ripleyâs K-function, L(r) - r function and the Pair Correlation Function (PCF), to show that they are invariant to such subsampling. We derive expressions for their stochastic estimators due to subsampling, and characterize the errors. The results can be extended to co-localization analysis as well. We then describe a method that estimate the true locations of points given the observed ones in clusters. We characterize the relative Mean Squared Error of the combined approach, and find that it can significantly reduce the errors in quantification. We apply these methods on data on clustering due to photoblinking of individual fluorophores, and data with redundant labeling. We then study the theoretical properties of a function that has been proposed as an estimator of cluster size. We also describe a method to identify the cluster model from data. SMLM provides single molecule resolution images of specific molecular species. Atomic Force Microscopy ( AFM), on the other hand, provides nonspecific, high resolution spatial profile information. Correlative AFM-SMLM can provide not only validation of SMLM, but also the complementary information so obtained can be used to design innovative experiments. We describe in vitro imaging of actin filaments with an AFM-SMLM correlative tool, that could provide information about sources of imaging inhomogeneity in SMLM. The tool were also used to image live mammalian cells, and can be used to obtain nanoscale information about mechanical properties of cells and also as a tool for nanomanipulation. Co-localization is a common spatial interaction quantification method used in biological imaging. It is possible to use tools from spatial statistics to obtain better statistical power on tests of spatial interaction, compared to conventional co-localization measures. Such tools can also handle SMLM data better, since they work on point patterns rather than images. We describe an ImageJ/Fiji plugin that implements a spatial statistics framework that extends the concept of co-localization, by means of a model based on Gibbs function of interaction potentials. We describe the application of this software on both confocal microscopy data of virus-endosomes, and SMLM data on GPCR protein-clathrin.

EPFL2015

MosaicIA: an ImageJ/Fiji plugin for spatial pattern and interaction analysis

Aleksandra Radenovic, Arun Shivanandan

Background: Analyzing spatial distributions of objects in images is a fundamental task in many biological studies. The relative arrangement of a set of objects with respect to another set of objects contains information about potential interactions between the two sets of objects. If they do not "feel" each other's presence, their spatial distributions are expected to be independent of one another. Spatial correlations in their distributions are indicative of interactions and can be modeled by an effective interaction potential acting between the points of the two sets. This can be used to generalize co-localization analysis to spatial interaction analysis. However, no user-friendly software for this type of analysis was available so far. Results: We present an ImageJ/Fiji plugin that implements the complete workflow of spatial pattern and interaction analysis for spot-like objects. The plugin detects objects in images, infers the interaction potential that is most likely to explain the observed pattern, and provides statistical tests for whether an inferred interaction is significant given the number of objects detected in the images and the size of the space within which they can distribute. We benchmark and demonstrate the present software using examples from confocal and PALM single-molecule microscopy. Conclusions: The present software greatly simplifies spatial interaction analysis for point patterns, and makes it available to the large user community of ImageJ and Fiji. The presented showcases illustrate the usage of the software.

BioMed Central2013

Analytical Methods, Correlative Microscopy and Software Tools for Quantitative Single Molecule Localization Microscopy

Arun Shivanandan

EPFL2015