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

Ponomarev, Gennady V.; Arlazarov, Vladimir V.; Gelfand, Mikhail S.; Kazanov, Marat D.

doi:10.1016/j.patcog.2013.09.027

Cited by 33 publications

(18 citation statements)

References 19 publications

(21 reference statements)

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“…The size of the datasets in some drug discovery applications ranges from ∼ 500 to ∼ 7 × 10 7 . The number of data samples varies from ∼ 250 to ∼ 6 × 10 4 in many digital pathology applications . Some cell biology applications have dataset sizes of the order ∼ 800.…”

Section: Discussionmentioning

confidence: 99%

“…NFE methods extract pre‐determined features from segmented cells and use them to classify images with a classifier algorithm . Cell images can be classified in their raw forms (e.g., pixel values) or can be summarized into some internal representation (or set of features) that may bear better discriminating information.…”

Section: Overview Of Image Classification Methodsmentioning

confidence: 99%

“…Numerical features‐based cell fluorescence pattern classification has been used many times before to detect different antinuclear auto‐antibodies relevant to systemic autoimmune diseases. Numerical features have been used in other examples to count different types of white blood cells in bone marrow and thereby help in the diagnosis of certain diseases, e.g., leukemia, acquired immunodeficiency syndrome, cancers, and so on.…”

Section: Overview Of Image Classification Methodsmentioning

confidence: 99%

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Cell Image Classification: A Comparative Overview

et al. 2020

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Cell image classification methods are currently being used in numerous applications in cell biology and medicine. Applications include understanding the effects of genes and drugs in screening experiments, understanding the role and subcellular localization of different proteins, as well as diagnosis and prognosis of cancer from images acquired using cytological and histological techniques. The article also reviews three main approaches for cell image classification most often used: numerical feature extraction, end-to-end classification with neural networks (NNs), and transport-based morphometry (TBM). In addition, we provide comparisons on four different cell imaging datasets to highlight the relative strength of each method. The results computed using four publicly available datasets show that numerical features tend to carry the best discriminative information for most of the classification tasks. Results also show that NN-based methods produce state-of-the-art results in the dataset that contains a relatively large number of training samples. Data augmentation or the choice of a more recently reported architecture does not necessarily improve the classification performance of NNs in the datasets with limited number of training samples. If understanding and visualization are desired aspects, TBM methods can offer the ability to invert classification functions, and thus can aid in the interpretation of results. These and other comparison outcomes are discussed with the aim of clarifying the advantages and disadvantages of each method. © 2020 International Society for Advancement of Cytometry Key terms image informatics; computational biology; cell biology; digital pathology Interpretation of images of cells has always played important roles in science and medicine. From their discovery in 1665, observation of the spatiotemporal characteristics of cells through microscopic technology has enabled us to better understand the structure of living cells, as well as how they perform certain functions (1,2). In addition, scientists have long used microscopes to evaluate the efficacy of different compounds for drug development (3-9). In medicine, as another example, the observation of cell morphology has long been used to discern malignancy in cancer cells (10-13).Cells are known to exhibit complex phenotypes such as differences in shape, gene expression, subcellular protein localization, and other qualities. In addition, cell cultures, tissues, and organs are known to exhibit complex heterogeneity of phenotypes. The combination of intricate phenotype differences together with their heterogeneous responses to different conditions (e.g., diseases) has made decoding biological processes an increasingly complex task. Thus computational approaches for analyzing images of cells have been used increasingly to aid in the task of decoding the complexity of biological processes. A common task useful in many practical situations is determining the category of a given cell or set of cells: a task known as cell classification.Auto...

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Section: Discussionmentioning

confidence: 99%

Section: Overview Of Image Classification Methodsmentioning

confidence: 99%

Section: Overview Of Image Classification Methodsmentioning

confidence: 99%

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Cell Image Classification: A Comparative Overview

et al. 2020

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“…Proposed descriptors include global statistics of grey-level distributions (e.g. grey-level co-occurrence matrices [19]), morphological measures of shape or topology [20], or combination of the two [21,22], as well as various formulations of local binary patterns. In particular, a novel variant of local binary patterns, namely Rotation Invariant Cooccurrence among adjacent LBPs (RIC-LBPs), was proposed by the winner of ICPR 2012 contest [23].…”

Section: Related Work and Contributions Of The Papermentioning

confidence: 99%

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

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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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“…Another interesting hybrid feature extraction method can be found in the work by Theodorakopoulos et al [16] where the authors have proposed the combination of the LBP and SIFT descriptors for the HEp-2 cells classification. Different other hand-crafted features can be seen in [17,18], and many others are listed in the quasi-exhaustive review made by Foggia et al [3].…”

Section: Introductionmentioning

confidence: 99%

A Deep Feature Extraction Method for HEp-2 Cell Image Classification

2018

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The automated and accurate classification of the images portraying the Human Epithelial cells of type 2 (HEp-2) represents one of the most important steps in the diagnosis procedure of many autoimmune diseases. The extreme intra-class variations of the HEp-2 cell images datasets drastically complicates the classification task. We propose in this work a classification framework that, unlike most of the state-of-the-art methods, uses a deep learning-based feature extraction method in a strictly unsupervised way. We propose a deep learning-based hybrid feature learning with two levels of deep convolutional autoencoders. The first level takes the original cell images as the inputs and learns to reconstruct them, in order to capture the features related to the global shape of the cells, and the second network takes the gradients of the images, in order to encode the localized changes in intensity (gray variations) that characterize each cell type. A final feature vector is constructed by combining the latent representations extracted from the two networks, giving a highly discriminative feature representation. The created features will be fed to a nonlinear classifier whose output will represent the type of the cell image. We have tested the discriminability of the proposed features on two of the most popular HEp-2 cell classification datasets, the SNPHEp-2 and ICPR 2016 datasets. The results show that the proposed features manage to capture the distinctive characteristics of the different cell types while performing at least as well as the actual deep learning-based state-of-the-art methods in terms of discrimination.

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

Cited by 33 publications

References 19 publications

Cell Image Classification: A Comparative Overview

Cell Image Classification: A Comparative Overview

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

A Deep Feature Extraction Method for HEp-2 Cell Image Classification

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