Unsupervised HEp-2 mitosis recognition in indirect immunofluorescence imaging

Tonti, Simone; Cataldo, Santa Di; Macii, Enrico; Ficarra, Elisa

doi:10.1109/embc.2015.7320282

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…However, their method showed a poor result for classifying the positive mitotic class, achieving a true positive rate of 51.7%. Alternatively, Tonti et al [ 31 ] suggested a completely unsupervised approach to differentiate the minority mitotic cell images from the remaining interphase cell images based on predefined rules on the morphological and texture GLCM [ 32 ] descriptors. The best classification accuracy from their method was achieved at 75.6%.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Related Workmentioning

confidence: 99%

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

et al. 2023

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“…The correct identification of the HEp-2 pattern helps identifying the type of antibody, hence it indirectly allows a differential diagnosis of the autoimmune disease. In the last few years, many researchers have exploited the analysis of HEp-2 textures to either perform the automated classification of HEp-2 patterns [22], [23], the automated segmentation of HEp-2 cells [24], [25] or the recognition of mitotic processes within the HEp-2 samples [26], [27], which are all important tasks in the ANA testing procedure.…”

Section: Texture Analysis In Biological Imagingmentioning

confidence: 99%

Mining textural knowledge in biological images: Applications, methods and trends

Cataldo

Ficarra

2017

Computational and Structural Biotechnology Journal

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Texture analysis is a major task in many areas of computer vision and pattern recognition, including biological imaging. Indeed, visual textures can be exploited to distinguish specific tissues or cells in a biological sample, to highlight chemical reactions between molecules, as well as to detect subcellular patterns that can be evidence of certain pathologies. This makes automated texture analysis fundamental in many applications of biomedicine, such as the accurate detection and grading of multiple types of cancer, the differential diagnosis of autoimmune diseases, or the study of physiological processes. Due to their specific characteristics and challenges, the design of texture analysis systems for biological images has attracted ever-growing attention in the last few years. In this paper, we perform a critical review of this important topic. First, we provide a general definition of texture analysis and discuss its role in the context of bioimaging, with examples of applications from the recent literature. Then, we review the main approaches to automated texture analysis, with special attention to the methods of feature extraction and encoding that can be successfully applied to microscopy images of cells or tissues. Our aim is to provide an overview of the state of the art, as well as a glimpse into the latest and future trends of research in this area.

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“…Hence, the PC module is completely independent from the IC module, in that it does not need to receive intensity as an input parameter. (ii) mitotic cell detection as reported in [36] is applied in order to identify and remove the mitotic cells (i.e. cells undergoing cellular division).…”

Section: Pattern Classifier (Pc)mentioning

confidence: 99%

ANAlyte: A modular image analysis tool for ANA testing with indirect immunofluorescence

Cataldo

Tonti

Bottino

et al. 2016

Computer Methods and Programs in Biomedicine

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Unlike most of the other works in the recent literature, ANAlyte aims at the automatization of all the major steps of ANA image analysis. Results on public benchmarks demonstrate that the tool can characterize HEp-2 slides in terms of intensity and fluorescent pattern with accuracy better or comparable with the state of the art techniques, even when such techniques are run on manually segmented cells. Hence, ANAlyte can be proposed as a valid solution to the problem of ANA testing automatization.

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Unsupervised HEp-2 mitosis recognition in indirect immunofluorescence imaging

Cited by 8 publications

References 10 publications

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

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

Mining textural knowledge in biological images: Applications, methods and trends

ANAlyte: A modular image analysis tool for ANA testing with indirect immunofluorescence

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