165 research outputs found
An automated pattern recognition system for classifying indirect immunofluorescence images for HEp-2 cells and specimens
AbstractImmunofluorescence antinuclear antibody tests are important for diagnosis and management of autoimmune conditions; a key step that would benefit from reliable automation is the recognition of subcellular patterns suggestive of different diseases. We present a system to recognize such patterns, at cellular and specimen levels, in images of HEp-2 cells. Ensembles of SVMs were trained to classify cells into six classes based on sparse encoding of texture features with cell pyramids, capturing spatial, multi-scale structure. A similar approach was used to classify specimens into seven classes. Software implementations were submitted to an international contest hosted by ICPR 2014 (Performance Evaluation of Indirect Immunofluorescence Image Analysis Systems). Mean class accuracies obtained on heldout test data sets were 87.1% and 88.5% for cell and specimen classification respectively. These were the highest achieved in the competition, suggesting that our methods are state-of-the-art. We provide detailed descriptions and extensive experiments with various features and encoding methods
Comparative Study of Human and Automated Screening for Antinuclear Antibodies by Immunofluorescence on HEp-2 Cells
Background: Several automated systems had been developed in order to reduce inter-observer variability in
indirect immunofluorescence (IIF) interpretation. We aimed to evaluate the performance of a processing system in
antinuclear antibodies (ANA) screening on HEp-2 cells.
Patients and Methods: This study included 64 ANA-positive sera and 107 ANA-negative sera that underwent IIF on two
commercial kits of HEp-2 cells (BioSystems® and Euroimmun®). IIF results were compared with a novel automated
interpretation system, the “CyclopusCADImmuno®” (CAD).
Results: All ANA-positive sera images were recognized as positive by CAD (sensitivity = 100%), while 17 (15.9%) of the
ANA-negative sera images were interpreted as positive (specificity = 84.1%), =0.799 (SD=0.045). Comparison of IIF
pattern determination between human and CAD system revealed on HEp-2 (BioSystems®), a complete concordance in
6 (9.37%) sera, a partial concordance (sharing of at least 1 pattern) in 42 (65.6%) cases and in 16 (25%) sera the
pattern interpretation was discordant. Similarly, on HEp-2 (Euroimmun®) the concordance in pattern interpretation was
total in 5 (7.8%) sera, partial in 39 (60.9%) and absent in 20 (31.25%). For both tested HEp-2 cells kits agreement was
enhanced for the most common patterns, homogenous, fine speckled and coarse speckled. While there was an issue in
identification of nucleolar, dots and nuclear membranous patterns by CAD.
Conclusion: Assessment of ANA by IIF on HEp-2 cells using the automated interpretation system, the
“CyclopusCADImmuno®” is a reliable method for positive/negative differentiation. Continuous integration of IIF images
would improve the pattern identification by the CAD
Deep CNN for IIF Images Classification in Autoimmune Diagnostics
The diagnosis and monitoring of autoimmune diseases are very important problem in medicine. The most used test for this purpose is the antinuclear antibody (ANA) test. An indirect immunofluorescence (IIF) test performed by Human Epithelial type 2 (HEp-2) cells as substrate antigen is the most common methods to determine ANA. In this paper we present an automatic HEp-2 specimen system based on a convolutional neural network method able to classify IIF images. The system consists of a module for features extraction based on a pre-trained AlexNet network and a classification phase for the cell-pattern association using six support vector machines and a k-nearest neighbors classifier. The classification at the image-level was obtained by analyzing the pattern prevalence at cell-level. The layers of the pre-trained network and various system parameters were evaluated in order to optimize the process. This system has been developed and tested on the HEp-2 images indirect immunofluorescence images analysis (I3A) public database. To test the generalisation performance of the method, the leave-one-specimen-out procedure was used in this work. The performance analysis showed an accuracy of 96.4% and a mean class accuracy equal to 93.8%. The results have been evaluated comparing them with some of the most representative works using the same database
HEP-2 CELL IMAGES FLUORESCENCE INTENSITY CLASSIFICATION TO DETERMINE POSITIVITY BASED ON NEURAL NETWORK AMIN
Nowadays, the recommended method for detection of anti-nuclear auto-antibodies is by using Indirect Immunofluorescence (IIF). The increasing of test demands on classification of Hep-2 cell images force the physicians to carry out the test faster, resulting bad quality results. IIF diagnosis requires estimating the fluorescence intensity of the serum and this will be observed. As there are subjective and inter/intra laboratory perception of the results, the development of computer-aided diagnosis (CAD) tools is used to support the decision. In this report, we propose the classification technique based on Artificial Neural Network (ANN) that can classify the Hep-2 cell images into 3 classes namely positive, negative and intermediate,specifically to determine the presence of antinuclear autoantibodies (ANA)
Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching
This paper describes a novel system for automatic classification of images
obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial
type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The
IIF protocol on HEp-2 cells has been the hallmark method to identify the
presence of ANAs, due to its high sensitivity and the large range of antigens
that can be detected. However, it suffers from numerous shortcomings, such as
being subjective as well as time and labour intensive. Computer Aided
Diagnostic (CAD) systems have been developed to address these problems, which
automatically classify a HEp-2 cell image into one of its known patterns (eg.
speckled, homogeneous). Most of the existing CAD systems use handpicked
features to represent a HEp-2 cell image, which may only work in limited
scenarios. We propose a novel automatic cell image classification method termed
Cell Pyramid Matching (CPM), which is comprised of regional histograms of
visual words coupled with the Multiple Kernel Learning framework. We present a
study of several variations of generating histograms and show the efficacy of
the system on two publicly available datasets: the ICPR HEp-2 cell
classification contest dataset and the SNPHEp-2 dataset.Comment: arXiv admin note: substantial text overlap with arXiv:1304.126
Automated Indirect Immunofluorescence Evaluation of Antinuclear Autoantibodies on HEp-2 Cells
Indirect immunofluorescence (IIF) on human epithelial (HEp-2) cells is considered as the gold standard screening method for the detection of antinuclear autoantibodies (ANA). However, in terms of automation and standardization, it has not been able to keep pace with most other analytical techniques used in diagnostic laboratories. Although there are already some automation solutions for IIF incubation in the market, the automation of result evaluation is still in its infancy. Therefore, the EUROPattern Suite has been developed as a comprehensive automated processing and interpretation system for standardized and efficient ANA detection by HEp-2 cell-based IIF. In this study, the automated pattern recognition was compared to conventional visual interpretation in a total of 351 sera. In the discrimination of positive from negative samples, concordant results between visual and automated evaluation were obtained for 349 sera (99.4%, kappa = 0.984). The system missed out none of the 272 antibody-positive samples and identified 77 out of 79 visually negative samples (analytical sensitivity/specificity: 100%/97.5%). Moreover, 94.0% of all main antibody patterns were recognized correctly by the software. Owing to its performance characteristics, EUROPattern enables fast, objective, and economic IIF ANA analysis and has the potential to reduce intra- and interlaboratory variability
Automated evaluation of autoantibodies on human epithelial-2 cells as an approach to standardize cell-based immunofluorescence tests
INTRODUCTION: Analysis of autoantibodies (AAB) by indirect immunofluorescence (IIF) is a basic tool for the serological diagnosis of systemic rheumatic disorders. Automation of autoantibody IIF reading including pattern recognition may improve intra- and inter-laboratory variability and meet the demand for cost-effective assessment of large numbers of samples. Comparing automated and visual interpretation, the usefulness for routine laboratory diagnostics was investigated. METHODS: Autoantibody detection by IIF on human epithelial-2 (HEp-2) cells was conducted in a total of 1222 consecutive sera of patients with suspected systemic rheumatic diseases from a university routine laboratory (n = 924) and a private referral laboratory (n = 298). IIF results from routine diagnostics were compared with a novel automated interpretation system. RESULTS: Both diagnostic procedures showed a very good agreement in detecting AAB (kappa = 0.828) and differentiating respective immunofluorescence patterns. Only 98 (8.0%) of 1222 sera demonstrated discrepant results in the differentiation of positive from negative samples. The contingency coefficients of chi-square statistics were 0.646 for the university laboratory cohort with an agreement of 93.0% and 0.695 for the private laboratory cohort with an agreement of 90.6%, P < 0.0001, respectively. Comparing immunofluorescence patterns, 111 (15.3%) sera yielded differing results. CONCLUSIONS: Automated assessment of AAB by IIF on HEp-2 cells using an automated interpretation system is a reliable and robust method for positive/negative differentiation. Employing novel mathematical algorithms, automated interpretation provides reproducible detection of specific immunofluorescence patterns on HEp-2 cells. Automated interpretation can reduce drawbacks of IIF for AAB detection in routine diagnostics providing more reliable data for clinicians
Original Approach for Automated Quantification of Antinuclear Autoantibodies by Indirect Immunofluorescence
International audienceIntroduction. Indirect immunofluorescence (IIF) is the gold standard method for the detection of antinuclear antibodies (ANA) which are essential markers for the diagnosis of systemic autoimmune rheumatic diseases. For the discrimination of positive and negative samples, we propose here an original approach named Immunofluorescence for Computed Antinuclear antibody Rational Evaluation (ICARE) based on the calculation of a fluorescence index (FI). Methods. We made comparison between FI and visual evaluations on 237 consecutive samples and on a cohort of 25 patients with SLE. Results. We obtained very good technical performance of FI (95% sensitivity, 98% specificity, and a kappa of 0.92), even in a subgroup of weakly positive samples. A significant correlation between quantification of FI and IIF ANA titers was found (Spearman's = 0.80, < 0.0001). Clinical performance of ICARE was validated on a cohort of patients with SLE corroborating the fact that FI could represent an attractive alternative for the evaluation of antibody titer. Conclusion. Our results represent a major step for automated quantification of IIF ANA, opening attractive perspectives such as rapid sample screening and laboratory standardization
“SEGMENTATION OF ANTI NEUTROPHIL CYTOPLASMIC ANTIBODIES (ANCA) IMAGES BASED ON WATERSHED AND WAVELET”
Autoimmune disease is a type of disease where immune system unable to tell between the good side and bad side which lead to the misguided attack on the healthy cells and tissues. Autoimmune disease can be classified to more than 80 types depending on the affected area. The test also varies according to the suspected type of disease. Some examples of the test are Enzyme-Linked Immunosorbent Assay (ELISA) test, Indirect Immunofluorescence (IIF) test of Antinuclear Antibody (ANA) by using HeP-2 Cells and IIF test for Anti Neutrophil Cytoplasmic Antibodies (ANCA). However in this project, author only focus on the ANCA images with two major staining patterns which are P-ANCA and C-ANCA. Currently the positivity of the images depends solely on the experience of the physician which led to variety of result and lack of reliability. Besides the time to get the result is time consuming. Thus an automatic classification system has been developed to overcome the manual process. The vital process inside the automatic system is the segmentation part. Many researchers suggest different techniques of segmentation to segment the ANCA images before being further processed. In this research, author focus on Watershed technique to segment the ANCA images by implementing the algorithm in Matlab. Author use Wavelet transform to suppress noise to avoid from over segmentation of the ANCA images. Using Rand Index method, the result of segmentations is verified. Combination of Watershed and Wavelet transform gives a very promising result. Recommendation for future work is to explore on automatic determination of noise variance inside images
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