Karyotype analysis by computer and its application to mutagenicity testing of environmental chemicals

Castleman, Kenneth R.; Melnyk, John; Frieden, H.J.; Persinger, G.; Wall, R. J.

doi:10.1016/0027-5107(76)90085-3

Cited by 9 publications

(3 citation statements)

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“…The use of computerized algorithms to aid in understanding biological processes is not new. The practice of karyotyping -the process of describing the number and appearance of chromosomes in a eukaryotic cell in order to identify genomic defects -has relied on automated procedures for over fifty years [62,63]. Understanding the specific structure and characteristics of an organism's genetic make-up can provide insights into developmental status.…”

Section: Cell Biologymentioning

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: Cell Biologymentioning

confidence: 99%

“…The dataset contains a large number(63,445) of segmented cell images from six different classes. This dataset is created by segmenting out single cell images from 948 specimen images of the ICPR 2014 HEp-2 cell classification contest dataset.…”

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Cell Image Classification: A Comparative Overview

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“…The first uses of computer vision systems in cellular analysis go back to the sixties, where research groups sought to replace human operator input in karyotyping (Gilbert, 1966;Castleman et al, 1976). The primary goal for eliminating visual inspection was to speed up the tedious processes of finding cells in mitosis, arranging the chromosome images into karyograms, and measuring and classifying features in the chromosome images for the purpose of detecting mutations and defects in the genome.…”

Section: Early and Recent Uses Of Computer Vision In Cell Biological mentioning

confidence: 99%

Computer Vision in Cell Biology

Danuser

2011

Cell

139

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Computer vision refers to the theory and implementation of artificial systems that extract information from images to understand their content. Although computers are widely used by cell biologists for visualization and measurement, interpretation of image content, i.e., the selection of events worth observing and the definition of what they mean in terms of cellular mechanisms, is mostly left to human intuition. This Essay attempts to outline roles computer vision may play and should play in image-based studies of cellular life.

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