Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks
Rahkonen, S., Koskinen, E., Pölönen, I., Heinonen, T., Ylikomi, T., Äyrämö, S., & Eskelinen, M. A. (2020). Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks. Journal of Medical Imaging, 7(2), Article 024001. https://doi.org/10.1117/1.JMI.7.2.024001
Published inJournal of Medical Imaging
© 2020 The Authors
New increasingly complex in vitro cancer cell models are being developed. These new models seem to represent the cell behavior in vivo more accurately and have better physiological relevance than prior models. An efficient testing method for selecting the most optimal drug treatment does not exist to date. One proposed solution to the problem involves isolation of cancer cells from the patients’ cancer tissue, after which they are exposed to potential drugs alone or in combinations to find the most optimal medication. To achieve this goal, methods that can efficiently quantify and analyze changes in tested cell are needed. Our study aimed to detect and segment cells and structures from cancer cell cultures grown on vascular structures in phase-contrast microscope images using U-Net neural networks to enable future drug efficacy assessments. We cultivated prostate carcinoma cell lines PC3 and LNCaP on the top of a matrix containing vascular structures. The cells were imaged with a Cell-IQ phase-contrast microscope. Automatic analysis of microscope images could assess the efficacy of tested drugs. The dataset included 36 RGB images and ground-truth segmentations with mutually not exclusive classes. The used method could distinguish vascular structures, cells, spheroids, and cell matter around spheroids in the test images. Some invasive spikes were also detected, but the method could not distinguish the invasive cells in the test images. ...
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Funding program(s)Public research networked with companies, TEKES
Additional information about fundingThe research has been cofunded by University of Jyväskylä, the Finnish Funding Agency for Innovation Tekes (Grant No. 1711/31/2016) and the Foundation of Jane and Aatos Erkko (Grant No. 170015).
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