Detection of Tumor Cell Spheroids from Co-cultures Using Phase Contrast Images and Machine Learning Approach

Neslihan Bayramoglu, Mika Kaakinen, Lauri Eklund, Malin Åkerfelt, Matthias Nees, Juho Kannala, Janne Heikkilä

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

4 Citations (Scopus)

Abstract

Automated image analysis is demanded in cell biology and drug development research. The type of microscopy is one of the considerations in the trade-offs between experimental setup, image acquisition speed, molecular labelling, resolution and quality of images. In many cases, phase contrast imaging gets higher weights in this optimization. And it comes at the price of reduced image quality in imaging 3D cell cultures. For such data, the existing state-of-the-art computer vision methods perform poorly in segmenting specific cell type. Low SNR, clutter and occlusions are basic challenges for blind segmentation approaches. In this study we propose an automated method, based on a learning framework, for detecting particular cell type in cluttered 2D phase contrast images of 3D cell cultures that overcomes those challenges. It depends on local features defined over super pixels. The method learns appearance based features, statistical features, textural features and their combinations. Also, the importance of each feature is measured by employing Random Forest classifier. Experiments show that our approach does not depend on training data and the parameters
Original languageEnglish
Title of host publicationProceedings
Subtitle of host publication22nd International Conference on Pattern Recognition, ICPR 2014
PublisherInstitute of Electrical and Electronic Engineers IEEE
Pages3345-3350
ISBN (Print)978-1-4799-5209-0
DOIs
Publication statusPublished - 2014
MoE publication typeA4 Article in a conference publication
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014

Conference

Conference22nd International Conference on Pattern Recognition, ICPR 2014
Abbreviated titleICPR 2014
CountrySweden
CityStockholm
Period24/08/1428/08/14

Fingerprint

Cell culture
Learning systems
Tumors
Cells
Cytology
Imaging techniques
Image acquisition
Labeling
Image analysis
Image quality
Computer vision
Microscopic examination
Classifiers
Pixels
Experiments

Cite this

Bayramoglu, N., Kaakinen, M., Eklund, L., Åkerfelt, M., Nees, M., Kannala, J., & Heikkilä, J. (2014). Detection of Tumor Cell Spheroids from Co-cultures Using Phase Contrast Images and Machine Learning Approach. In Proceedings: 22nd International Conference on Pattern Recognition, ICPR 2014 (pp. 3345-3350). Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/ICPR.2014.576
Bayramoglu, Neslihan ; Kaakinen, Mika ; Eklund, Lauri ; Åkerfelt, Malin ; Nees, Matthias ; Kannala, Juho ; Heikkilä, Janne. / Detection of Tumor Cell Spheroids from Co-cultures Using Phase Contrast Images and Machine Learning Approach. Proceedings: 22nd International Conference on Pattern Recognition, ICPR 2014. Institute of Electrical and Electronic Engineers IEEE, 2014. pp. 3345-3350
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Bayramoglu, N, Kaakinen, M, Eklund, L, Åkerfelt, M, Nees, M, Kannala, J & Heikkilä, J 2014, Detection of Tumor Cell Spheroids from Co-cultures Using Phase Contrast Images and Machine Learning Approach. in Proceedings: 22nd International Conference on Pattern Recognition, ICPR 2014. Institute of Electrical and Electronic Engineers IEEE, pp. 3345-3350, 22nd International Conference on Pattern Recognition, ICPR 2014, Stockholm, Sweden, 24/08/14. https://doi.org/10.1109/ICPR.2014.576

Detection of Tumor Cell Spheroids from Co-cultures Using Phase Contrast Images and Machine Learning Approach. / Bayramoglu, Neslihan; Kaakinen, Mika; Eklund, Lauri; Åkerfelt, Malin; Nees, Matthias; Kannala, Juho; Heikkilä, Janne.

Proceedings: 22nd International Conference on Pattern Recognition, ICPR 2014. Institute of Electrical and Electronic Engineers IEEE, 2014. p. 3345-3350.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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AU - Nees, Matthias

AU - Kannala, Juho

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AB - Automated image analysis is demanded in cell biology and drug development research. The type of microscopy is one of the considerations in the trade-offs between experimental setup, image acquisition speed, molecular labelling, resolution and quality of images. In many cases, phase contrast imaging gets higher weights in this optimization. And it comes at the price of reduced image quality in imaging 3D cell cultures. For such data, the existing state-of-the-art computer vision methods perform poorly in segmenting specific cell type. Low SNR, clutter and occlusions are basic challenges for blind segmentation approaches. In this study we propose an automated method, based on a learning framework, for detecting particular cell type in cluttered 2D phase contrast images of 3D cell cultures that overcomes those challenges. It depends on local features defined over super pixels. The method learns appearance based features, statistical features, textural features and their combinations. Also, the importance of each feature is measured by employing Random Forest classifier. Experiments show that our approach does not depend on training data and the parameters

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Bayramoglu N, Kaakinen M, Eklund L, Åkerfelt M, Nees M, Kannala J et al. Detection of Tumor Cell Spheroids from Co-cultures Using Phase Contrast Images and Machine Learning Approach. In Proceedings: 22nd International Conference on Pattern Recognition, ICPR 2014. Institute of Electrical and Electronic Engineers IEEE. 2014. p. 3345-3350 https://doi.org/10.1109/ICPR.2014.576