Indirect Immunofluorescence Images Research Articles

B-073 The Clinical Utility of Reporting AC-2 Pattern With Anti-DFS70 Antibody

Abstract Background The dense fine speckled (DFS) pattern (AC-2) is often observed in antinuclear antibody (ANA) indirect immunofluorescence (IIF) assay, and the related anti-DFS70 antibody is known to be negatively associated with a systemic autoimmune rheumatic disease (SARD). Identifying the AC-2 pattern is challenging, as it could be generally confused with the AC-1, AC-4, AC-5, or AC-29 patterns displaying stained metaphase plates or speckled nuclei. Since the anti-DFS70 antibody is not included in routine autoantibody tests, the clinical utility of the AC-2 pattern and anti-DFS70 antibody is not adequately applied in the laboratory and clinical practice. We evaluated the prevalence of anti-DFS70 and other autoantibodies in samples presenting AC-2 pattern in ANA IIF assay. Additionally, we analyzed the association of anti-DFS70 antibodies and clinical features in patients with AC-2 patterns. Method We retrospectively reviewed the ANA IIF images of 1839 samples made by an automated ANA analyzer classified as AC-1, AC-2, AC-4, AC-5, and AC-29 patterns. AC-1, AC-2, AC-4, AC-5, and AC-29 patterns comprised 60.5% of all ANA positives (1839/3039). Among them, the AC-2 pattern was identified in 18.5% (341/1839) by two experts, and only 197 with residual samples were available for further evaluation. Those 197 samples were tested for anti-DFS70 antibodies with enzyme-linked immunosorbent assay (ELISA) and line immunoblot assay (LIA). They were also tested for 22 other autoantibodies (dsDNA, nucleosomes, histones, SS-A/Ro-52, SS-B, RNP/Sm, Sm, Mi-2α, Mi-2β, Ku, CENP-A, CENP-B, Sp100, PML, Scl-70, PM/Scl100, PM/Scl75, RP11, RP155, gp210, PCNA) with LIA. Clinical information from 197 patients was obtained from a retrospective review of electronic medical records. Results Anti-DFS70 antibody prevalence by ELISA and LIA was 32.0% (63/197) and 19.8% (39/197), respectively. The agreement percentage between the two assays was 81.7%, with a kappa value of 0.53 (95% confidence interval: 0.40–0.66). Of the 69 DFS70+ samples (as per ELISA or LIA), the coexistence of other autoantibodies was found in 81.2% (56/69). Of the 128 DFS70- samples, 90.6% (116/128) revealed other autoantibodies. The three most frequent autoantibodies (except anti-DFS70) in 197 patients with AC-2 patterns were anti-SS-A/Ro-52 (30.5%, 60/197), anti-nucleosomes (27.4%, 54/197), and anti-histones (20.3%, 40/197). There was no significant difference in the prevalence of the other 22 autoantibodies between DFS70+ and DFS70− patients. The anti-DFS70 prevalence in the SARD group was 33.3% (50/150), and in the non-SARD was 40.4% (19/47) without significant difference. However, of the 69 DFS70+ patients, the isolated anti-DFS70 was found more frequently in the non-SARD group (36.8%, 7/19) than in the SARD group (12.0%, 6/50) (P = 0.0346). Conclusion The anti-DFS70 positive rate in AC-2 pattern samples is lower than a third, and the prevalence is affected by different detection assays. Along with the AC-2 pattern, just reporting the presence of anti-DFS70 is not valuable for SARD exclusion. Therefore, it is important to co-examine other autoantibodies, such as anti-SS-A, anti-nucleosomes, and anti-histones, to confirm the isolated anti-DFS70.

A deep learning self-attention cross residual network with Info-WGANGP for mitotic cell identification in HEp-2 medical microscopic images

BackgroundThe identification of human epithelial type-2 mitotic cell patterns in the indirect immunofluorescence images (IIF HEp-2) is a critical step for autoimmune diseases computer-aided diagnosis (CAD) systems. Recognition of HEp-2 cells in the mitotic phase is clinically vital for validating the HEp-2 samples and assisting in diagnosing specimens with mixed patterns. Typically, mitotic cells are rarely observed in the HEp-2 specimen images, resulting in a significant skewness of the available medical datasets towards the majority of interphase (non-mitotic) patterns. MethodsIn this paper, a deep learning framework is proposed based on a self-attention deep cross-residual network with an efficient generative adversarial network (GAN). The proposed framework consists of two consecutive steps. First, the problem of imbalanced data of minority mitotic against the majority interphase cells is remedied using Info-WGANGP approach in combination with the conventional data augmentation methods. Second, a downstream end-to-end deep learning Att-DCRNet is developed to classify the mitotic and interphase cell patterns of the IIF HEp-2 medical images. A comprehensive experimental study is performed to validate the effectiveness of the proposed framework against other state-of-the-art methods over the public medical dataset of UQ-SNP_HEp-2 Task-3. ResultsThe proposed framework demonstrates competitive classification results attaining a maximum performance of 84.10% F1-score, 84.70% Matthew's correlation coefficient (MCC), and 99.0% balanced class accuracy (BcA), which proves its applicability for automatically supporting an accurate diagnosis decision regarding the HEp-2 mitotic and interphase cell patterns. The source code is available at this link: https://github.com/Anaam-dl/AttDCRNet_with_InfoWGANGP.

Computer-aided classification of indirect immunofluorescence patterns on esophagus and split skin for the detection of autoimmune dermatoses.

Autoimmune bullous dermatoses (AIBD) are rare diseases that affect human skin and mucous membranes. Clinically, they are characterized by blister formation and/or erosions. Depending on the structures involved and the depth of blister formation, they are grouped into pemphigus diseases, pemphigoid diseases, and dermatitis herpetiformis. Classification of AIBD into their sub-entities is crucial to guide treatment decisions. One of the most sensitive screening methods for initial differentiation of AIBD is the indirect immunofluorescence (IIF) microscopy on tissue sections of monkey esophagus and primate salt-split skin, which are used to detect disease-specific autoantibodies. Interpretation of IIF patterns requires a detailed examination of the image by trained professionals automating this process is a challenging task with these highly complex tissue substrates, but offers the great advantage of an objective result. Here, we present computer-aided classification of esophagus and salt-split skin IIF images. We show how deep networks can be adapted to the specifics and challenges of IIF image analysis by incorporating segmentation of relevant regions into the prediction process, and demonstrate their high accuracy. Using this semi-automatic extension can reduce the workload of professionals when reading tissue sections in IIF testing. Furthermore, these results on highly complex tissue sections show that further integration of semi-automated workflows into the daily workflow of diagnostic laboratories is promising.

Automatic Segmentation and Classification for Antinuclear Antibody Images Based on Deep Learning.

Antinuclear antibodies (ANAs) testing is the main serological diagnosis screening test for autoimmune diseases. ANAs testing is conducted principally by the indirect immunofluorescence (IIF) on human epithelial cell-substrate (HEp-2) protocol. However, due to its high variability and human subjectivity, there is an insistent need to develop an efficient method for automatic image segmentation and classification. This article develops an automatic segmentation and classification framework based on artificial intelligence (AI) on the ANA images. The Otsu thresholding method and watershed segmentation algorithm are adopted to segment IIF images of cells. Moreover, multiple texture features such as scale-invariant feature transform (SIFT), local binary pattern (LBP), cooccurrence among adjacent LBPs (CoALBP), and rotation invariant cooccurrence among adjacent LBPs (RIC-LBP) are utilized. Firstly, this article adopts traditional machine learning methods such as support vector machine (SVM), k-nearest neighbor algorithm (KNN), and random forest (RF) and then uses ensemble classifier (ECLF) combined with soft voting rules to merge these machine learning methods for classification. The deep learning method InceptionResNetV2 is also utilized to train on the classification of cell images. Eventually, the best accuracy of 0.9269 on the Changsha dataset and 0.9635 on the ICPR 2016 dataset for the traditional methods is obtained by a combination of SIFT and RIC-LBP with the ECLF classifier, and the best accuracy obtained by the InceptionResNetV2 is 0.9465 and 0.9836 separately, which outperforms other schemes.

Joint segmentation and classification task via adversarial network: Application to HEp-2 cell images

The recognition of epithelial type 2 (HEp-2) cells has become an important tool for detecting autoimmune diseases diagnosis by using indirect immunofluorescence (IIF) images. For many medical image tasks, the accurate segmentation is considered as the primary step for the classification task, which inspires researchers to solve problems by jointly implementing multiple tasks. For this reason, we devise a hybrid network architecture, which combines the segmentation and classification networks for the final classification of HEp-2 cells, where a multi-task generative adversarial networks (GANs) is employed to produce accurate segmentation masks for improving the latter classification performance. Specifically, the devised generation and discrimination subnetworks establish the GANs architecture for the accurate segmentation masks of HEp-2 cells. Also, the original images are utilized as the conditional input, which are concatenated by the generated masks and the ground-truth to train the discriminator for two tasks: the first task determines whether the generated mask is a ground-truth or not and the other one distinguishes the category of the processed HEp-2 cell image. Furthermore, the ResNet-34 and MobileNetv3 are used as segmentation and classification base network, respectively. We modify the MobileNetv3 structure by adding the channel of the middle outputs, which is called augmented channel MobileNetv3 (ACM-Net). Both the discriminator and classifier share the weights of ACM-Net. The extensive experiments on the public ICPR 2016 task1 dataset show that the proposed hybrid-task based GANs structure can obtain promising segmentation and classification performance via jointly training mode, which achieves a Dice of 97.04% and for segmentation and a prediction accuracy of 98.82% for classification, respectively.

Rough-Bayesian approach to select class-pair specific descriptors for HEp-2 cell staining pattern recognition

One of the important problems in computer-aided diagnosis of connective tissue disease is automatic recognition of staining patterns present in HEp-2 cells. In this regard, the paper introduces a novel approach for the recognition of staining patterns by HEp-2 cell indirect immunofluorescence image analysis. The proposed method assumes that a fixed set of local texture descriptors or scales may not be effective for classifying staining patterns into multiple classes. A particular set of descriptors or scales may be significant for classifying a pair of classes, but may not be relevant for other pairs of classes. The proposed approach, therefore, first selects a set of local texture descriptors under appropriate scales for each class-pair, and then forms the final feature set for multiple classes from the relevant descriptors of all possible pairs of classes. A novel framework, termed as Rough-Bayesian model, is introduced to evaluate the relevance of a descriptor and/or a scale. It is based on the merits of rough sets and Bayes decision theory. During the selection of relevant descriptor and/or scale, the proposed method takes care of the presence of both noisy pixels in an HEp-2 cell image and noisy HEp-2 cell images in a staining pattern class. The support vector machine is used to predict the staining patterns present in HEp-2 cell images. The performance of the proposed method, along with a comparison with state-of-the-art methods, is demonstrated on several HEp-2 cell image databases. An important finding is that the accuracy for classifying HEp-2 cell images is significantly increased if class-pair specific descriptors under appropriate scales are considered, instead of selecting a uniform set of descriptors and scales for multiple classes.

Correction: Staining Pattern Classification of Antinuclear Autoantibodies Based on Block Segmentation in Indirect Immunofluorescence Images.

[This corrects the article DOI: 10.1371/journal.pone.0113132.].

Selection of relevant texture descriptors for recognition of HEp-2 cell staining patterns

One of the important applications of computer-aided diagnosis is the detection of connective tissue disorders through automatic classification of antinuclear autoantibodies (ANAs). The recognition of ANAs is primarily done by analyzing indirect immunofluorescence (IIF) images of human epithelial type 2 (HEp-2) cells. In this regard, the paper introduces a novel approach for automatic classification of ANAs by staining pattern recognition of HEp-2 cell IIF images. Considering a set of HEp-2 cell images, the proposed method selects a set of relevant local texture descriptors for a pair of staining pattern classes, as well as identifies a set of important features corresponding to each relevant descriptor. The set of features for multiple classes is obtained from each of the important feature sets selected under various relevant local texture descriptors for all possible class-pairs. The relevance of a descriptor is evaluated based on the theory of rough hypercuboid approach, while the important feature set of a local descriptor is formed by reducing the impact of both noisy pixels present in an HEp-2 cell image and noisy HEp-2 cell images in a staining pattern class. Finally, the support vector machine is used to recognize one of the known staining patterns present in IIF images. The effectiveness of the proposed staining pattern recognition method, along with a comparison with related approaches, is illustrated on two benchmark databases of HEp-2 cell images using different classifiers and experimental set-up. The results show that the proposed approach performs significantly better than existing methods, with respect to both classification accuracy and F1 score, irrespective of the databases and classifiers used.

Towards the automatic segmentation of HEp-2 cells in indirect immunofluorescence images using an efficient filtering based approach

The computer aided analysis of Indirect-Immunofluorescence (IIF) images is important for the differential diagnosis of several autoimmune diseases. A fully automatic approach consists in segmentation of individual cells in IIF images and subsequently its classification into various pattern types. This paper explores the segmentation of HEp2 cell in IIF images through the use of a filtering based approach. Our algorithm is based on a local convergence filter named as Sliding Band Filter (SBF). We propose a modified SBF that is capable of handling the low contrast, noise and illumination variations peculiar to IIF images. In addition, we follow a simple algorithmic pipeline and achieve better accuracy as compared to several state of the art segmentation algorithms on standard HEp2 image dataset.

A study on indirect immunofluorescence image classification methods for bioinformatics

The indirect immunofluorescence (IIF) test with human epithelial type-2 (HEp-2) cells as substrates is the gold standard for anti-nuclear antibodies (ANA) test to diagnose autoimmune diseases. The specialists in the laboratory visually examine the specimen under microscope to recognise the staining patterns and generate the report. So ANA test is subjective and needs systemic automation for bioinformatics. In this view international benchmarking initiatives were organised by IAPR in the last six years. In this paper the state of the art on IIF HEp-2 cells classification task was analysed. This paper highlighted the original aspects with the detailed discussion of the published methods. Design choice verses performance was analysed.

A study on indirect immunofluorescence image classification methods for bioinformatics

The indirect immunofluorescence (IIF) test with human epithelial type-2 (HEp-2) cells as substrates is the gold standard for anti-nuclear antibodies (ANA) test to diagnose autoimmune diseases. The specialists in the laboratory visually examine the specimen under microscope to recognise the staining patterns and generate the report. So ANA test is subjective and needs systemic automation for bioinformatics. In this view international benchmarking initiatives were organised by IAPR in the last six years. In this paper the state of the art on IIF HEp-2 cells classification task was analysed. This paper highlighted the original aspects with the detailed discussion of the published methods. Design choice verses performance was analysed.

A Research on Automation Diagonsis of Pattern Recognition in Stained HEP-2 Cells

Diagnosis of autoimmune diseases can be achieved via Indirect Immunofluorescence (IIF) images using human epithelial (HEp-2) cell as substrate in laboratory. The automation of this diagnosis method is still challenging because of using various liquids to fix the HEp-2 cells in the slides. Due to various fixation methods, nuclear morphology of cell suffers high variability. This survey reviews all the difficulties in the analysis and recognition of pattern recognition and surveys various image processing techniques which leads to the automation diagnosis. This work consist of advantages and disadvantages of various procedures. Eventually, comparison of their corresponding results are presented. I assure that this initial work may attract many medical image processing researchers to enter into this field.

Data Security Using 512 Bits Symmetric Key Based Cryptography In Cloud Computing System

Diagnosis of autoimmune diseases can be achieved via Indirect Immunofluorescence (IIF) images using human epithelial (HEp-2) cell as substrate in laboratory. The automation of this diagnosis method is still challenging because of using various liquids to fix the HEp-2 cells in the slides. Due to various fixation methods, nuclear morphology of cell suffers high variability. This survey reviews all the difficulties in the analysis and recognition of pattern recognition and surveys various image processing techniques which leads to the automation diagnosis. This work consist of advantages and disadvantages of various procedures. Eventually, comparison of their corresponding results are presented. I assure that this initial work may attract many medical image processing researchers to enter into this field.

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.

CanSuR: a robust method for staining pattern recognition of HEp-2 cell IIF images

The recognition of staining patterns present in human epithelial type 2 (HEp-2) cells helps to diagnose connective tissue disease. In this context, the paper introduces a robust method, termed as CanSuR, for automatic recognition of antinuclear autoantibodies by HEp-2 cell indirect immunofluorescence (IIF) image analysis. The proposed method combines the advantages of a new sequential supervised canonical correlation analysis (CCA), introduced in this paper, with the theory of rough hypercuboid approach. While the proposed CCA efficiently combines the local textural information of HEp-2 cells, derived from various scales of rotation-invariant local binary patterns, the relevant and significant features of HEp-2 cell for staining pattern recognition are extracted using rough hypercuboid approach. Finally, the support vector machine, with radial basis function kernel, is used to recognize one of the known staining patterns present in IIF images. The effectiveness of the proposed staining pattern recognition method, along with a comparison with related approaches, is demonstrated on MIVIA, SNP and ICPR HEp-2 cell image databases. An important finding is that the proposed method performs significantly better than state-of-the art methods, on three HEp-2 cell image databases with respect to both classification accuracy and F1 score.

Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification

Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the final association to the positive or negative classes. This system has been developed and the classifier was trained on a database implemented by the AIDA (AutoImmunité, Diagnostic Assisté par ordinateur) project. The method proposed here has been tested on a public part of the same database, consisting of 2080 IIF images. The performance analysis showed an accuracy of fluorescent intensity around 93%. The results have been evaluated by comparing them with some of the most representative state-of-the-art works, demonstrating the quality of the system in the intensity classification of HEp-2 images.

An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentation methods, based on the Hough transform for ellipses, and on an active contours model. In order to classify the HEp-2 cells, six SVM and one k-nearest neighbors (KNN)classifiers were developed. The system was tested on a public database consisting of 2080 IIF images. Unlike almost all work presented on this topic, the proposed system automatically addresses all phases of the HEp-2 image analysis process. All results have been evaluated by comparing them with some of the most representative state-of-the-art work, demonstrating the goodness of the system in the characterization of HEp-2 images.

The actomyosin network is influenced by NMHC IIA and regulated by CrpF46, which is involved in controlling cell migration

When a cell migrates, the centrosome positions between the nucleus and the leading edge of migration via the microtubule system. The protein CrpF46 (centrosome-related protein F46) has a known role during mitosis and centrosome duplication. However, how CrpF46 efficiently regulates centrosome-related cell migration is unclear. Here, we report that knockdown of CrpF46 resulted in the disruption of microtubule arrangement, with impaired centrosomal reorientation, and slowed down cell migration. In cells that express low levels of CrpF46, stress fibers were weakened, which could be rescued by recovering Flag-CrpF46. We also found that CrpF46 interacted with non-muscle myosin high chain IIA (NMHC IIA) and that its three coiled-coil domains are pivotal for its binding to NMHC IIA. Additionally, analyses of phosphorylation of NMHC IIA and RLC (regulatory light chain) demonstrated that CrpF46 was associated with myosin IIA during filament formation. Indirect immunofluorescence images indicated that NM IIA filaments were inhibited when CrpF46 was under-expressed. Thus, CrpF46 regulates cell migration by centrosomal reorientation and altering the function of the actomyosin network by controlling specific phosphorylation of myosin.

Automated approach for indirect immunofluorescence images classification based on unsupervised clustering method

Autoimmune diseases (ADs) are a collection of many complex disorders of unknown aetiology resulting in immune responses to self‐antigens and are thought to result from interactions between genetic and environmental factors. ADs collectively are amongst the most prevalent diseases in the U.S., affecting at least 7% of the population. The diagnosis of ADs is very complex, the standard screening methods provides seeking and recognizing of Antinuclear Antibodies (ANA) by Indirect ImmunoFluorescence (IIF) based on HEp‐2 cells. In this paper an automatic system able to identify and classify the Centromere pattern is presented. The method is based on the grouping of centromeres present on the cells through a clustering K‐means algorithm. The performances were obtained on two public database of IIF images (A.I.D.A. and MIVIA). Our results showed a sensitivity for image of (90 ± 5)% and a Accuracy equal to (98.0 ± 0.5)%. Results demonstrate that the system is able to identify and classify Centromere pattern with accuracy better or comparable with some representative state of the art works. Moreover, it should be noted that for the classification phase the works used for the comparison used an expert‐manual segmentation while, in the present work, the segmentation was obtained automatically.

Deep cross residual network for HEp-2 cell staining pattern classification

Many computer-aided systems have been developed for Human epithelial type 2 (HEp-2) cell classification recently, but there is still a big performance gap between them and specialist doctors. Inspired by the recent successes of convolutional neural network, we proposed a deep cross residual network (DCRNet) for HEp-2 cell classification. A cross connection based residual block was proposed to increase the information flow among different network layers. We used two benchmark datasets to evaluate our system. The state-of-art results, i.e. the average class accuracy of 80.8% in the International Conference on Pattern Recognition (ICPR) 2012 dataset and the mean class accuracy of 85.1% in the Indirect Immunofluorescence Image (I3A) dataset, were achieved. Our result on the ICPR 2012 dataset is so far the best among all works reported in the literature. Our algorithm was winner of the most recent ICPR 2016 contest and the accuracy beat all of the top performers in the previous International Conference on Image Processing (ICIP) 2013 and the ICPR 2014 contests.