publication . Article . Preprint . 2020

Cell Image Classification: A Comparative Overview.

Shifat-E-Rabbi, M; Yin, Xuwang; Fitzgerald, Cailey Elizabeth; Rohde, Gustavo K.;
Open Access
  • Published: 10 Feb 2020 Journal: Cytometry Part A, volume 97, pages 347-362 (issn: 1552-4922, eissn: 1552-4930, Copyright policy)
  • Publisher: Wiley
Abstract
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. We review three different approaches for cell image classification: numerical feature extraction, end to end classification with neural networks, and transport-based morphometry. In addition, we provide comparisons on four different cell imaging datasets to highlight the rela...
Subjects
free text keywords: Pathology and Forensic Medicine, Cell Biology, Histology, Quantitative Biology - Quantitative Methods
Funded by
NIH| Automated High-Throuput Estimation and Modeling of Protein Network Distributions
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01GM090033-02
  • Funding stream: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
74 references, page 1 of 5

[1] Robert Hooke. Micrographia: Or Some Physiological Descriptions of Minute Bodies Made by Magnifying Glasses. With Observations and Inquiries Thereupon. By R. Hooke, Fellow of the Royal Society. Jo. Martyn, and Ja. Allestry, printers to the Royal Society, 1961.

[2] Paolo Mazzarello. A unifying concept: the history of cell theory. Nature cell biology, 1(1):E13, 1999.

[3] Zachary E Perlman, Michael D Slack, Yan Feng, Timothy J Mitchison, Lani F Wu, and Steven J Altschuler. Multidimensional drug pro ling by automated microscopy. Science, 306(5699):1194{1198, 2004.

[4] Christian Scheeder, Florian Heigwer, and Michael Boutros. Machine learning and image-based pro ling in drug discovery. Current Opinion in Systems Biology, 2018. [OpenAIRE]

[5] Jinghai J. Xu, Peter V. Henstock, Margaret C. Dunn, Arthur R. Smith, Je rey R. Chabot, and David de Graaf. Cellular imaging predictions of clinical drug-induced liver injury. Toxicological Sciences, 105(1):97{105, 2008.

[6] Saurav Basu, Soheil Kolouri, and Gustavo K Rohde. Detecting and visualizing cell phenotype di erences from microscopy images using transport-based morphometry. Proceedings of the National Academy of Sciences, 111(9):3448{3453, 2014.

[7] Shinsuke Ohnuki, Satomi Oka, Satoru Nogami, and Yoshikazu Ohya. High-Content, ImageBased Screening for Drug Targets in Yeast. PLOS ONE, 5(4):1{11, 04 2010.

[8] Juan C Caicedo, Sam Cooper, Florian Heigwer, Scott Warchal, Peng Qiu, Csaba Molnar, Aliaksei S Vasilevich, Joseph D Barry, Harmanjit Singh Bansal, Oren Kraus, Mathias Wawer, Lassi Paavolainen, Markus D Herrmann, Mohammad Rohban, Jane Hung, Holger Hennig, John Concannon, Ian Smith, Paul A Clemons, Shantanu Singh, Paul Rees, Peter Horvath, Roger G Linington, and Anne E Carpenter. Data-analysis strategies for image-based cell pro ling. Nature Methods, 14:849, aug 2017.

[9] Vasanth Siruvallur Murali, Bo-Jui Chang, Reto Fiolka, Gaudenz Danuser, Murat Can Cobanoglu, and Erik Welf. An image-based assay to quantify changes in proliferation and viability upon drug treatment in 3D microenvironments. bioRxiv, 2018.

[18] Tsu-Yi Hsieh, Yi-Chu Huang, Chia-Wei Chung, and Yu-Len Huang. Hep-2 cell classi cation in indirect immuno uorescence images. In Information, Communications and Signal Processing, 2009. ICICS 2009. 7th International Conference on, pages 1{4. IEEE, 2009.

[19] Arnold Wiliem, Yongkang Wong, Conrad Sanderson, Peter Hobson, Shaokang Chen, and Brian C Lovell. Classi cation of human epithelial type 2 cell indirect immuno uoresence images via codebook based descriptors. In Applications of Computer Vision (WACV), 2013 IEEE Workshop on, pages 95{102. IEEE, 2013. [OpenAIRE]

[20] Ilker Ersoy, Filiz Bunyak, Jing Peng, and Kannappan Palaniappan. Hep-2 cell classi cation in IIF images using shareboost. In Pattern Recognition (ICPR), 2012 21st International Conference on, pages 3362{3365. IEEE, 2012. [OpenAIRE]

[21] Ryusuke Nosaka and Kazuhiro Fukui. Hep-2 cell classi cation using rotation invariant co-occurrence among local binary patterns. Pattern Recognition, 47(7):2428{2436, 2014.

[23] BC Ko, JW Gim, and JY Nam. Cell image classi cation based on ensemble features and random forest. Electronics Letters, 47(11):638{639, 2011.

[24] Wei-Liang Tai, Rouh-Mei Hu, Han CW Hsiao, Rong-Ming Chen, and Je rey JP Tsai. Blood cell image classi cation based on hierarchical svm. In Multimedia (ISM), 2011 IEEE International Symposium on, pages 129{136. IEEE, 2011.

74 references, page 1 of 5
Abstract
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. We review three different approaches for cell image classification: numerical feature extraction, end to end classification with neural networks, and transport-based morphometry. In addition, we provide comparisons on four different cell imaging datasets to highlight the rela...
Subjects
free text keywords: Pathology and Forensic Medicine, Cell Biology, Histology, Quantitative Biology - Quantitative Methods
Funded by
NIH| Automated High-Throuput Estimation and Modeling of Protein Network Distributions
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01GM090033-02
  • Funding stream: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
74 references, page 1 of 5

[1] Robert Hooke. Micrographia: Or Some Physiological Descriptions of Minute Bodies Made by Magnifying Glasses. With Observations and Inquiries Thereupon. By R. Hooke, Fellow of the Royal Society. Jo. Martyn, and Ja. Allestry, printers to the Royal Society, 1961.

[2] Paolo Mazzarello. A unifying concept: the history of cell theory. Nature cell biology, 1(1):E13, 1999.

[3] Zachary E Perlman, Michael D Slack, Yan Feng, Timothy J Mitchison, Lani F Wu, and Steven J Altschuler. Multidimensional drug pro ling by automated microscopy. Science, 306(5699):1194{1198, 2004.

[4] Christian Scheeder, Florian Heigwer, and Michael Boutros. Machine learning and image-based pro ling in drug discovery. Current Opinion in Systems Biology, 2018. [OpenAIRE]

[5] Jinghai J. Xu, Peter V. Henstock, Margaret C. Dunn, Arthur R. Smith, Je rey R. Chabot, and David de Graaf. Cellular imaging predictions of clinical drug-induced liver injury. Toxicological Sciences, 105(1):97{105, 2008.

[6] Saurav Basu, Soheil Kolouri, and Gustavo K Rohde. Detecting and visualizing cell phenotype di erences from microscopy images using transport-based morphometry. Proceedings of the National Academy of Sciences, 111(9):3448{3453, 2014.

[7] Shinsuke Ohnuki, Satomi Oka, Satoru Nogami, and Yoshikazu Ohya. High-Content, ImageBased Screening for Drug Targets in Yeast. PLOS ONE, 5(4):1{11, 04 2010.

[8] Juan C Caicedo, Sam Cooper, Florian Heigwer, Scott Warchal, Peng Qiu, Csaba Molnar, Aliaksei S Vasilevich, Joseph D Barry, Harmanjit Singh Bansal, Oren Kraus, Mathias Wawer, Lassi Paavolainen, Markus D Herrmann, Mohammad Rohban, Jane Hung, Holger Hennig, John Concannon, Ian Smith, Paul A Clemons, Shantanu Singh, Paul Rees, Peter Horvath, Roger G Linington, and Anne E Carpenter. Data-analysis strategies for image-based cell pro ling. Nature Methods, 14:849, aug 2017.

[9] Vasanth Siruvallur Murali, Bo-Jui Chang, Reto Fiolka, Gaudenz Danuser, Murat Can Cobanoglu, and Erik Welf. An image-based assay to quantify changes in proliferation and viability upon drug treatment in 3D microenvironments. bioRxiv, 2018.

[18] Tsu-Yi Hsieh, Yi-Chu Huang, Chia-Wei Chung, and Yu-Len Huang. Hep-2 cell classi cation in indirect immuno uorescence images. In Information, Communications and Signal Processing, 2009. ICICS 2009. 7th International Conference on, pages 1{4. IEEE, 2009.

[19] Arnold Wiliem, Yongkang Wong, Conrad Sanderson, Peter Hobson, Shaokang Chen, and Brian C Lovell. Classi cation of human epithelial type 2 cell indirect immuno uoresence images via codebook based descriptors. In Applications of Computer Vision (WACV), 2013 IEEE Workshop on, pages 95{102. IEEE, 2013. [OpenAIRE]

[20] Ilker Ersoy, Filiz Bunyak, Jing Peng, and Kannappan Palaniappan. Hep-2 cell classi cation in IIF images using shareboost. In Pattern Recognition (ICPR), 2012 21st International Conference on, pages 3362{3365. IEEE, 2012. [OpenAIRE]

[21] Ryusuke Nosaka and Kazuhiro Fukui. Hep-2 cell classi cation using rotation invariant co-occurrence among local binary patterns. Pattern Recognition, 47(7):2428{2436, 2014.

[23] BC Ko, JW Gim, and JY Nam. Cell image classi cation based on ensemble features and random forest. Electronics Letters, 47(11):638{639, 2011.

[24] Wei-Liang Tai, Rouh-Mei Hu, Han CW Hsiao, Rong-Ming Chen, and Je rey JP Tsai. Blood cell image classi cation based on hierarchical svm. In Multimedia (ISM), 2011 IEEE International Symposium on, pages 129{136. IEEE, 2011.

74 references, page 1 of 5
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue