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

Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images

Identifying Human Epithelial Type 2 (HEp-2) mitotic cells is a crucial procedure in anti-nuclear antibodies (ANAs) testing, which is the standard protocol for detecting connective tissue diseases (CTD). Due to the low throughput and labor-subjectivity of the ANAs' manual screening test, there is a need to develop a reliable HEp-2 computer-aided diagnosis (CAD) system. The automatic detection of mitotic cells from the microscopic HEp-2 specimen images is an essential step to support the diagnosis process and enhance the throughput of this test. This work proposes a deep active learning (DAL) approach to overcoming the cell labeling challenge. Moreover, deep learning detectors are tailored to automatically identify the mitotic cells directly in the entire microscopic HEp-2 specimen images, avoiding the segmentation step. The proposed framework is validated using the I3A Task-2 dataset over 5-fold cross-validation trials. Using the YOLO predictor, promising mitotic cell prediction results are achieved with an average of 90.011% recall, 88.307% precision, and 81.531% mAP. Whereas, average scores of 86.986% recall, 85.282% precision, and 78.506% mAP are obtained using the Faster R-CNN predictor. Employing the DAL method over four labeling rounds effectively enhances the accuracy of the data annotation, and hence, improves the prediction performance. The proposed framework could be practically applicable to support medical personnel in making rapid and accurate decisions about the mitotic cells' existence

MITOTIC HEP-2 CELL RECOGNIITON USING SUPPORT VECTOR MACHINE UNDER CLASS SKEW

A person with an autoimmune diseases will became hypersensitive to the surrounding that other normal person would usually not consider at all such as an allergy. This reaction happened when our immune system recognise our normal tissue as a dangerous foreign elements and proceed to attack them. The presence of antinuclear autoantibodies (ANA) in a patient serum can be detected by using the Indirect Immunofluorescence (IIF) image. By adding the mitotic cells into the well, the level of accuracy of the results achieved can be increased. The mitotic cells itself plays a crucial role in diagnosing an autoimmune diseases. This paper will focuses on the extracting the features of a mitotic HEp-2 cell in order to determine the presence of an ANA by noting the cells fluorescent-stained pattern, their intensity and also the presence of the mitotic cell itself. A skewed distribution of both mitotic and non-mitotic cells in the samples will also be considered to ensure the practicality of the project. To assist in the objectives, all the techniques used are explain in more detailed in this paper along with the result obtained by simulation from MATLAB for every steps from pre-processing to user interface menu. The procedures for the recognition of mitotic cells are image acquisition, pre-processing, segmentation, feature extraction and classification. The results obtained were tested using HEp-2 cell image datasets from MIVIA and from collaboration with Hospital Universiti Sains Malaysia (HUSM). The feature extractor used is the gray level co-occurrence matrix (GLCM) and classified using support vector machine (SVM) which will be presented in the RESULTS section

Step-wise assembly, maturation and dynamic behavior of the human CENP-P/O/R/Q/U kinetochore sub-complex

Kinetochores are multi-protein megadalton assemblies that are required for attachment of microtubules to centromeres and, in turn, the segregation of chromosomes in mitosis. Kinetochore assembly is a cell cycle regulated multi-step process. The initial step occurs during interphase and involves loading of the 15-subunit constitutive centromere associated complex (CCAN), which contains a 5-subunit (CENP-P/O/R/Q/U) sub-complex. Here we show using a fluorescent three-hybrid (F3H) assay and fluorescence resonance energy transfer (FRET) in living mammalian cells that CENP-P/O/R/Q/U subunits exist in a tightly packed arrangement that involves multifold protein-protein interactions. This sub-complex is, however, not pre-assembled in the cytoplasm, but rather assembled on kinetochores through the step-wise recruitment of CENP-O/P heterodimers and the CENP-P, -O, -R, -Q and -U single protein units. SNAP-tag experiments and immuno-staining indicate that these loading events occur during S-phase in a manner similar to the nucleosome binding components of the CCAN, CENP-T/W/N. Furthermore, CENP-P/O/R/Q/U binding to the CCAN is largely mediated through interactions with the CENP-N binding protein CENP-L as well as CENP-K. Once assembled, CENP-P/O/R/Q/U exchanges slowly with the free nucleoplasmic pool indicating a low off-rate for individual CENP-P/O/R/Q/U subunits. Surprisingly, we then find that during late S-phase, following the kinetochore-binding step, both CENP-Q and -U but not -R undergo oligomerization. We propose that CENP-P/O/R/Q/U self-assembles on kinetochores with varying stoichiometry and undergoes a pre-mitotic maturation step that could be important for kinetochores switching into the correct conformation necessary for microtubule-attachment

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

Variabilidade no reconhecimento de padrões de imunofluorescência indireta em diferentes marcas de lâminas HEp-2

INTRODUCTION: Indirect immunofluorescence on HEp-2 cells is considered the gold standard for the detection of autoantibodies against cellular antigens. However, the culture conditions, cell fixation and permeabilization processes interfere directly in the preservation and spatial distribution of antigens. Therefore, one can assume that certain peculiarities in the processing of cellular substrate may affect the recognition of indirect immunofluorescence patterns associated with several autoantibodies. OBJECTIVE: To evaluate a panel of serum samples representing nuclear, nucleolar, cytoplasmic, mitotic apparatus, and chromosome plate patterns on HEp-2 cell substrates from different suppliers. MATERIALS AND METHODS: Seven blinded observers, independent from the three selected reference centers, evaluated 17 samples yielding different nuclear, nucleolar, cytoplasmic and mitotic apparatus patterns on HEp-2 cell slides from eight different brands. The slides were coded to maintain confidentiality of both brands and participating centers. RESULTS: The 17 HEp-2 cell patterns were identified on most substrates. Nonetheless, some slides showed deficit in the expression of several patterns: nuclear coarse speckled/U1-ribonucleoprotein associated with antibodies against RNP (U1RNP), centromeric protein F (CENP-F), proliferating cell nuclear antigen (PCNA), cytoplasmic fine speckled associated with anti-Jo-1 antibodies (histidyl synthetase), nuclear mitotic apparatus protein 1 (NuMA-1) and nuclear mitotic apparatus protein 2 (NuMA-2). CONCLUSION: Despite the overall good quality of the assessed HEp-2 substrates, there was considerable inconsistency in results among different commercial substrates. The variations may be due to the evaluated batches, hence generalizations cannot be made as to the respective brands. It is recommended that each new batch or new brand be tested with a panel of reference sera representing the various patterns.INTRODUÇÃO: A imunofluorescência indireta (IFI) utilizando células HEp-2 como substrato antigênico é o teste padrão-ouro para a pesquisa de autoanticorpos contra antígenos celulares. Contudo, as condições de cultivo, fixação e permeabilização celular interferem diretamente na preservação e na distribuição espacial dos antígenos. Portanto, pode-se presumir que distintas condições no preparo das células possam interferir no reconhecimento dos padrões de imunofluorescência associados aos diversos autoanticorpos. OBJETIVO: Avaliar um painel de amostras de soro representativo de padrões nuclear, nucleolar, citoplasmático, de aparelho mitótico e de placa cromossômica em substratos de células HEp-2 de diferentes fornecedores. MATERIAIS E MÉTODOS: Sete observadores blindados e independentes de três centros de referência avaliaram 17 amostras que apresentavam diferentes padrões nucleares, nucleolares, citoplasmáticos e associados ao aparelho mitótico em lâminas com células HEp-2 de oito procedências. As lâminas foram codificadas para manter a confidencialidade das marcas, bem como dos centros participantes. RESULTADOS: Os 17 padrões de imunofluorescência em células HEp-2 foram reconhecidos na maioria dos substratos. No entanto, alguns substratos mostraram déficit na apresentação de alguns padrões (nuclear pontilhado grosso/U1-ribonucleoprotein associado a anticorpos contra o RNP (U1 ribonucleoproteína), sugestivo da presença de anticorpos anti-CENP-F (proteína centromérica F), sugestivo de anticorpos contra antígenos de célula em proliferação (proliferating cell nuclear antigen [PCNA]), citoplasmático pontilhado fino associado a anticorpos anti-Jo-1 (histidil sintetase), anti-NuMA-1 (nuclear mitotic apparatus protein 1) e anti-NuMA-2 (nuclear mitotic apparatus protein 2). CONCLUSÃO: Em que pese a boa qualidade geral dos substratos avaliados, existe divergência nos resultados obtidos entre os diferentes substratos comerciais. As variações observadas podem ser devidas aos lotes avaliados, portanto não se pode generalizar para as respectivas marcas. Recomenda-se que cada novo lote ou marca de lâmina sejam testados com diferentes soros referência representativos dos diversos padrões.Grupo Fleury Research and Development DepartmentUNIFESPPUC-Goiás Pharmacy DepartmentPUC-Goiás Medicine DepartmentPadrão Laboratório ClínicoFMUSP Hospital das Clínicas Division of LaboratoriesHospital Israelita Albert Einstein Clinical Pathology DepartmentHospital Israelita Albert Einstein Clinical Pathology Department Immunopathology SectorUNIFESP Immuno-Rheumatology LaboratoryGrupo Fleury Immunology DepartmentUNIFESP, Immuno-Rheumatology LaboratorySciEL

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%)