Computer-assisted classification of HEp-2 immunofluorescence patterns in autoimmune diagnostics

Sack, Ulrich; Knoechner, Stephan; Warschkau, Holger; Pigla, Ullrich; Emmrich, Frank; Kamprad, Manja

doi:10.1016/s1568-9972(03)00067-3

Cited by 85 publications

(61 citation statements)

References 23 publications

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“…In particular, in the area of autoantibodies testing some recent papers have presented CAD tools for the classification of HEp-2 images [10,11,12,13,14], which are used to detect other autoimmune diseases. To our knowledge no works in the literature propose solutions for automatic classification of CL slides.…”

Section: Background and Motivationsmentioning

confidence: 99%

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Analysis and Classification of Crithidia Luciliae Fluorescent Images

Soda

Onofri

Rigon

et al. 2009

Image Analysis and Processing – ICIAP 2009

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Abstract. Autoantibody tests based on Crithidia Luciliae (CL) substrate are the recommended method to detect Systemic Lupus Erythematosus (SLE), a very serious sickness further to be classified as an invalidating chronic disease. CL is an unicellular organism containing a strongly tangled mass of circular dsDNA, named as kinetoplast, whose fluorescence determines the positiveness to the test. Conversely, the staining of other parts of cell body is not a disease marker, thus representing false positive fluorescence. Such readings are subjected to several issues limiting the reproducibility and reliability of the method, as the photo-bleaching effect and the inter-observer variability. Hence, Computer-Aided Diagnosis (CAD) tools can support physicians decision. In this paper we propose a system to classify CL wells based on a three stages recognition approach, which classify single cell, images and, finally, the well. The fusion of such different information permits to reduce the misclassifications effect. The approach has been successfully tested on an annotated dataset, proving its feasibility.

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Section: Background and Motivationsmentioning

confidence: 99%

“…To our knowledge, only recent papers dealing with CAD tools for autoantibodies tests based on the HEp-2 substrate 1 can be found in the literature [10,11,12,13,14]. In this paper we focus on the development of a decision system able to classify anti-dsDNA images based on the Crithidia Luciliae substrate.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Classification of Crithidia Luciliae Fluorescent Images

Soda

Onofri

Rigon

et al. 2009

Image Analysis and Processing – ICIAP 2009

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“…This necessitates automation of the interpretation of ANA HEp-2 cell images. Several computerbased methods for classification of cell images [12][13][14][15] as well as preprocessing segmentation methods [16] were proposed in the literature, but they have not been benchmarked.…”

Section: Introductionmentioning

confidence: 99%

ANA HEp-2 cells image classification using number, size, shape and localization of targeted cell regions

Ponomarev

Arlazarov

Gelfand

et al. 2014

Pattern Recognition

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a b s t r a c tThe ANA HEp-2 medical test is a powerful tool in autoimmune disease diagnostics. The last step of this test, the interpretation of immunofluorescent images by trained experts, represents a potential source of errors and could theoretically be replaced by automated methods. Here we present a fully automatic method for recognition of types of immunofluorescent images produced by the ANA HEp-2 medical test. The proposed method makes use of the difference in number, size, shape and localization of cell regions that are targeted by the antinuclear antibodies -the humoral components of immune system that bind human antigens as a result of the immune system malfunction. The method extracts morphological properties of stained cell regions using a combination of thresholding-based and thresholding-less approaches and applies a conventional machine-learning algorithm for image classification.

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“…Indeed, first, medical doctors verify the correctness of well preparation process by detecting at least one fluorescent mitotic cell. Second, mitotic cells provide information on image staining pattern since they match with certain kinds of IIF patterns, including all stainings of antigens with different distribution throughout the cell cycle, such as midbody, CENP-F, mitotic splindle, centriole/centrosome and NuMA staining [4]. Since the demand of autoimmune laboratory tests has recently increased and ANAs detection in routine practice is far from being standardised [3], recent interests have been directed towards the development of computer-aided-diagnosis (CAD) systems supporting IIF diagnostic procedure.…”

Section: Introductionmentioning

confidence: 99%

“…Since the demand of autoimmune laboratory tests has recently increased and ANAs detection in routine practice is far from being standardised [3], recent interests have been directed towards the development of computer-aided-diagnosis (CAD) systems supporting IIF diagnostic procedure. Investigated topics covered the areas of image acquisition [5,6], image segmentation [7,8,9] and fluorescence intensity classification [10] as well as staining pattern recognition [4,9,11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Mitotic HEp-2 Cells Recognition under Class Skew

Percannella

Soda

Vento

2011

Image Analysis and Processing – ICIAP 2011

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Abstract. Indirect immunofluorescence (IIF) is the recommended method to diagnose the presence of antinuclear autoantibodies in patient serum. A main step of the diagnostic procedure requires to detect mitotic cells in the well under examination. However, such cells rarely occur in comparison to other cells and, hence, traditional recognition algorithms fail in this task since they cannot cope with large differences between the number of samples in each class, resulting in a low predictive accuracy over the minority class. In this paper we present a system for mitotic cells recognition based on multiobjective optimisation, which is able to handle their low a priori probability. It chooses between the output of a classifier trained on the original skewed distribution and the output of a classifier trained according to a learning method addressing the course of imbalanced data. This choice is driven by a parameter whose value maximises, on a validation set, two objective functions, i.e. the global accuracy and the accuracies for each class. The approach has been evaluated on an annotated dataset of mitotic cells and successfully compared to five learning methods applying four different classification paradigms.

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Computer-assisted classification of HEp-2 immunofluorescence patterns in autoimmune diagnostics

Cited by 85 publications

References 23 publications

Analysis and Classification of Crithidia Luciliae Fluorescent Images

Analysis and Classification of Crithidia Luciliae Fluorescent Images

ANA HEp-2 cells image classification using number, size, shape and localization of targeted cell regions

Mitotic HEp-2 Cells Recognition under Class Skew

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