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Comparison of Four Automatic Classifiers for Cancer Cell Phenotypes Using M-Phase Features Extracted from Brightfield Microscopy Images

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High Performance Computing (CARLA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1087))

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Abstract

In our in vitro study to model and understand the regulation networks that control the live and death of the cells, it is fundamental to quantify the contribution of each of the cancer cell’ phenotypes: apoptosis, cell cycle arrest, DNA damage repair, and DNA damage proliferation. For that, an automatic microscope is used to generate several images of cell populations using brightfield microscopy. In the scientific literature, several methods to extract features from microscopy images are available, but mostly for fluorescence or contrast phase microscopy, which have the disadvantage of being phototoxic to the cells, and therefore unsuitable for our study. In this paper a successful method to automatically extract and classify the phenotypes of cancer cells is presented. The method uses features extracted automatically from the M-phase (mitosis) of cells from images obtained by brightfield microscopy. The classification results are validated by comparing them with the correct manually annotated classes for each instance. Four different classifiers: Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), k-Nearest Neighbours (kNN), and Random Forests (RF) are compared using standard comparison metrics, such as precision, recall and F1-score. It is finally shown that the LDA classifier provided the best results, reaching an overall f1-score of 0.78 and an overall weighted f1-score of 0.88.

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Acknowledgments

The present paper is a partial result of a research project funded by CONARE with a FEES grant entitled Genomic Functional Analysis of Cancer Cells by Interference RNA for the Identification of Regulation Networks Associated to Proliferation and Death in Response to Genotoxic Chemotherapy with id 803-B8-653, registered at the Research Center for Tropical Diseases (CIET) and the Department of Electrical Engineering (EIE), both from UCR.

And a special thanks to the PRIS-Lab and LQT members for their help and support during the present work.

Author information

Authors and Affiliations

  1. PRIS-Lab: Pattern Recognition and Intelligent Systems Laboratory, Department of Electrical Engineering, School of Engineering, Universidad de Costa Rica (UCR), San José, Costa Rica

    Francisco Siles & Andrés Mora-Zúñga

  2. LabQT: Tumoral Chemosensitivity Laboratory, Research Center on Tropical Diseases (CIET), School of Microbiology, Universidad de Costa Rica (UCR), San José, Costa Rica

    Steve Quiros

Authors
  1. Francisco Siles
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  2. Andrés Mora-Zúñga
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  3. Steve Quiros
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Corresponding author

Correspondence to Francisco Siles .

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Editors and Affiliations

  1. Costa Rica Institute of Technology, Cartago, Costa Rica

    Juan Luis Crespo-Mariño

  2. Costa Rica Institute of Technology, Cartago, Costa Rica

    Esteban Meneses-Rojas

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Siles, F., Mora-Zúñga, A., Quiros, S. (2020). Comparison of Four Automatic Classifiers for Cancer Cell Phenotypes Using M-Phase Features Extracted from Brightfield Microscopy Images. In: Crespo-Mariño, J., Meneses-Rojas, E. (eds) High Performance Computing. CARLA 2019. Communications in Computer and Information Science, vol 1087. Springer, Cham. https://doi.org/10.1007/978-3-030-41005-6_28

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  • DOI: https://doi.org/10.1007/978-3-030-41005-6_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41004-9

  • Online ISBN: 978-3-030-41005-6

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