88 research outputs found
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
Preliminary results of the project A.I.D.A. (Auto Immunity: Diagnosis Assisted by computer)
In this paper, are presented the preliminary results of the A.I.D.A. (Auto Immunity: Diagnosis
Assisted by computer) project which is developed in the frame of the cross-border cooperation Italy-Tunisia.
According to the main objectives of this project, a database of interpreted Indirect ImmunoFluorescence (IIF)
images on HEp 2 cells is being collected thanks to the contribution of Italian and Tunisian experts involved in
routine diagnosis of autoimmune diseases. Through exchanging images and double reporting; a Gold Standard
database, containing around 1000 double reported IIF images with different patterns including negative tests,
has been settled. This Gold Standard database has been used for optimization of a computing solution (CADComputer
Aided Detection) and for assessment of its added value in order to be used along with an
immunologist as a second reader in detection of auto antibodies for autoimmune disease diagnosis. From the
preliminary results obtained, the CAD appeared more powerful than junior immunologists used as second
readers and may significantly improve their efficacy
Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project
Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of
Indirect ImmunoFluorescence (IIF)method, and performed by analyzing patterns and fluorescence intensity. This paper introduces
the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border
cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images
and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold
Standard database is used for optimization of aCAD(Computer AidedDetection) solution and for the assessment of its added value,
in order to be applied along with an Immunologist as a second Reader in detection of autoantibodies. This CAD system is able to
identify on IIF images the fluorescence intensity and the fluorescence pattern. Preliminary results show that CAD, used as second
Reader, appeared to perform better than Junior Immunologists and hence may significantly improve their efficacy; compared with
two Junior Immunologists, the CAD system showed higher Intensity Accuracy (85,5% versus 66,0% and 66,0%), higher Patterns
Accuracy (79,3% versus 48,0% and 66,2%), and higher Mean Class Accuracy (79,4% versus 56,7% and 64.2%)
Applying Textural Features to the Classification of HEp-2 Cell Patterns in IIF images
The analysis of anti-nuclear antibodies in HEp-2
cells by indirect immunofluorescence (IIF) is fundamental
for the diagnosis of important immune pathologies;
in particular, classifying the staining pattern of the cell
is critical for the differential diagnosis of several types
of diseases. Current tests based on human evaluation
are time-consuming and suffer from very high variability,
which impacts on the reliability of the results. As
a solution to this problem, in this work we propose a
technique that performs automated classification of the
staining pattern. Our method combines textural feature
extraction and a two-step feature selection scheme to
select a limited number of image attributes that are best
suited to the classification purpose and then recognizes
the staining pattern by means of a Support Vector Machine
module. Experiments on IIF images showed that
our method is able to identify staining patterns with average
accuracy of about 87%
HEp-2 Cell Classification with heterogeneous classes-processes based on K-Nearest Neighbours
We present a scheme for the feature extraction and classification of the fluorescence staining patterns of HEp-2 cells in IIF images. We propose a set of
complementary processes specific to each class of patterns to search. Our set of processes consists of preprocessing,features extraction and classification. The choice of methods, features and parameters was performed
automatically, using the Mean Class Accuracy (MCA) as a figure of merit. We extract a large number (108) of features able to fully characterize the staining pattern of HEp-2 cells. We propose a classification approach based
on two steps: the first step follows the one-against-all(OAA) scheme, while the second step follows the one-against-one (OAO) scheme. To do this, we needed to implement 21 KNN classifiers: 6 OAA and 15 OAO.
Leave-one-out image cross validation method was used for the evaluation of the results
An Automatic Indirect Immunofluorescence Cell Segmentation System
Indirect immunofluorescence (IIF) with HEp-2 cells has been used for the detection of antinuclear autoantibodies (ANA) in systemic autoimmune diseases. The ANA testing allows us to scan a broad range of autoantibody entities and to describe them by distinct fluorescence patterns. Automatic inspection for fluorescence patterns in an IIF image can assist physicians, without relevant experience, in making correct diagnosis. How to segment the cells from an IIF image is essential in developing an automatic inspection system for ANA testing. This paper focuses on the cell detection and segmentation; an efficient method is proposed for automatically detecting the cells with fluorescence pattern in an IIF image. Cell culture is a process in which cells grow under control. Cell counting technology plays an important role in measuring the cell density in a culture tank. Moreover, assessing medium suitability, determining population doubling times, and monitoring cell growth in cultures all require a means of quantifying cell population. The proposed method also can be used to count the cells from an image taken under a fluorescence microscope
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
Interpretation of immunofluorescence slides by deep learning techniques: anti-nuclear antibodies case study
Nowadays, diseases are increasing in numbers and severity by the hour.
Immunity diseases, affecting 8\% of the world population in 2017 according to
the World Health Organization (WHO), is a field in medicine worth attention due
to the high rate of disease occurrence classified under this category. This
work presents an up-to-date review of state-of-the-art immune diseases
healthcare solutions. We focus on tackling the issue with modern solutions such
as Deep Learning to detect anomalies in the early stages hence providing health
practitioners with efficient tools. We rely on advanced deep learning
techniques such as Convolutional Neural Networks (CNN) to fulfill our objective
of providing an efficient tool while providing a proficient analysis of this
solution. The proposed solution was tested and evaluated by the immunology
department in the Principal Military Hospital of Instruction of Tunis, which
considered it a very helpful tool
- …