Quantitative analysis methods for studying fenestrations in liver sinusoidal endothelial cells. A comparative study

Liver Sinusoidal Endothelial Cells (LSEC) line the hepatic vasculature providing blood filtration via transmembrane nanopores called fenestrations. These structures are 50−300 nm in diameter, which is below the resolution limit of a conventional light microscopy. To date, there is no standardized method of fenestration image analysis. With this study, we provide and compare three different approaches: manual measurements, a semi-automatic (threshold-based) method, and an automatic method based on user-friendly open source machine learning software. Images were obtained using three super resolution techniques – atomic force microscopy (AFM), scanning electron microscopy (SEM), and structured illumination microscopy (SIM). Parameters describing fenestrations such as diameter, area, roundness, frequency, and porosity were measured. Finally, we studied the user bias by comparison of the data obtained by five different users applying provided analysis methods

Development and validation of 'AutoRIF': Software for the automated analysis of radiation-induced foci

Copyright @ 2012 McVean et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.Background: The quantification of radiation-induced foci (RIF) to investigate the induction and subsequent repair of DNA double strands breaks is now commonplace. Over the last decade systems specific for the automatic quantification of RIF have been developed for this purpose, however to ask more mechanistic questions on the spatio-temporal aspects of RIF, an automated RIF analysis platform that also quantifies RIF size/volume and relative three-dimensional (3D) distribution of RIF within individual nuclei, is required. Results: A java-based image analysis system has been developed (AutoRIF) that quantifies the number, size/volume and relative nuclear locations of RIF within 3D nuclear volumes. Our approach identifies nuclei using the dynamic Otsu threshold and RIF by enhanced Laplacian filtering and maximum entropy thresholding steps and, has an application ‘batch optimisation’ process to ensure reproducible quantification of RIF. AutoRIF was validated by comparing output against manual quantification of the same 2D and 3D image stacks with results showing excellent concordance over a whole range of sample time points (and therefore range of total RIF/nucleus) after low-LET radiation exposure. Conclusions: This high-throughput automated RIF analysis system generates data with greater depth of information and reproducibility than that which can be achieved manually and may contribute toward the standardisation of RIF analysis. In particular, AutoRIF is a powerful tool for studying spatio-temporal relationships of RIF using a range of DNA damage response markers and can be run independently of other software, enabling most personal computers to perform image analysis. Future considerations for AutoRIF will likely include more complex algorithms that enable multiplex analysis for increasing combinations of cellular markers.This article is made available through the Brunel Open Access Publishing Fund

Automated identification of Fos expression

The concentration of Fos, a protein encoded by the immediate-early gene c-fos, provides a measure of synaptic activity that may not parallel the electrical activity of neurons. Such a measure is important for the difficult problem of identifying dynamic properties of neuronal circuitries activated by a variety of stimuli and behaviours. We employ two-stage statistical pattern recognition to identify cellular nuclei that express Fos in two-dimensional sections of rat forebrain after administration of antipsychotic drugs. In stage one, we distinguish dark-stained candidate nuclei from image background by a thresholding algorithm and record size and shape measurements of these objects. In stage two, we compare performance of linear and quadratic discriminants, nearest-neighbour and artificial neural network classifiers that employ functions of these measurements to label candidate objects as either Fos nuclei, two touching Fos nuclei or irrelevant background material. New images of neighbouring brain tissue serve as test sets to assess generalizability of the best derived classification rule, as determined by lowest cross-validation misclassification rate. Three experts, two internal and one external, compare manual and automated results for accuracy assessment. Analyses of a subset of images on two separate occasions provide quantitative measures of inter- and intra-expert consistency. We conclude that our automated procedure yields results that compare favourably with those of the experts and thus has potential to remove much of the tedium, subjectivity and irreproducibility of current Fos identification methods in digital microscopy

Assessment of algorithms for mitosis detection in breast cancer histopathology images

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists

Characterisation of the Physical Chemical Processes Using the Fractal and Harmonic Analysis

Existuje mnoho různých způsobů jak analyzovat disperzní systémy a fyzikálně chemické processy ke kterým v takových systémech dochází. Tato práce byla zaměřena na charakterizaci těchto procesů pomocí metod harmonické fraktální analýzy. Obrazová data sledovaných systémů byly analyzovány pomocí waveletové analýzy. V průběhu práce byly navrženy různé optimalizace samotné analýzy, převážně zaměřené na odstranění manuálních operací během analýzy a tyto optimalizace byly také inkorporovány do softérového vybavení pro Harmonickou Fraktální Analýzu HarFA, který je vyvíjen na Fakultě chemické, VUT Brno.There are many different ways to characterize the dispersed systems and processes occuring in such systems. This work focuses on use of Fractal properties of such systems to describe the physical and chemical processes occuring in such systems. The Fractal properties are calculated from the image data of the systems under the observation using the Wavelet analysis. Since the Harmonic Fractal Analysis can be relatively easily automated, the work also focuses on algorithmisation of the analysis and the removal all manual steps from the process. The automation have been performed by incorporating all the findings into the software for Harmonic Fractal Analysis HarFA developed at the Faculty of Chemistry, BUT.

Diagnosis of leukemia disease based on enhanced virtual neural network

White Blood Cell (WBC) cancer or leukemia is one of the serious cancers that threaten the existence of human beings. In spite of its prevalence and serious consequences, it is mostly diagnosed through manual practices. The risks of inappropriate, sub-standard and wrong or biased diagnosis are high in manual methods. So, there is a need exists for automatic diagnosis and classification method that can replace the manual process. Leukemia is mainly classified into acute and chronic types. The current research work proposed a computer-based application to classify the disease. In the feature extraction stage, we use excellent physical properties to improve the diagnostic system’s accuracy, based on Enhanced Color Co-Occurrence Matrix. The study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network (EVNN) classification. The proposed method achieved optimum accuracy in detection and classification of leukemia from WBC images. Thus, the study results establish the superiority of the proposed method in automated diagnosis of leukemia. The values achieved by the proposed method in terms of sensitivity, specificity, accuracy, and error rate were 97.8%, 89.9%, 76.6%, and 2.2%, respectively. Furthermore, the system could predict the disease in prior through images, and the probabilities of disease detection are also highly optimistic