A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Detection of tumor cell spheroids from co-cultures using phase contrast images and machine learning approach
Tekijät: Bayramoglu N, Kaakinen M, Eklund L, Akerfelt M, Nees M, Kannala J, Heikkila J
Toimittaja: IEEE
Konferenssin vakiintunut nimi: International Conference on Pattern Recognition (ICPR)
Julkaisuvuosi: 2014
Journal: International Conference on Pattern Recognition
Kokoomateoksen nimi: 2014 22nd International Conference on Pattern Recognition (ICPR)
Tietokannassa oleva lehden nimi: 2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
Lehden akronyymi: INT C PATT RECOG
Aloitussivu: 3345
Lopetussivu: 3350
Sivujen määrä: 6
eISBN: 978-1-4799-5209-0
ISSN: 1051-4651
DOI: https://doi.org/10.1109/ICPR.2014.576
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 superpixels. 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.
