A1 Journal article (refereed)
Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks (2020)

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

JYU authors or editors

Publication details

All authors or editors: Rahkonen, Samuli; Koskinen, Emilia; Pölönen, Ilkka; Heinonen, Tuula; Ylikomi, Timo; Äyrämö, Sami; Eskelinen, Matti A.

Journal or series: Journal of Medical Imaging

ISSN: 2329-4302

eISSN: 2329-4310

Publication year: 2020

Volume: 7

Issue number: 2

Article number: 024001

Publisher: SPIE

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1117/1.JMI.7.2.024001

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/68543

Additional information: Corrections to this article: J. of Medical Imaging, 7(2), 029801 (2020). https://doi.org/10.1117/1.JMI.7.2.029801

Abstract

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.

Keywords: cancer cells; in vitro method; cell culture; microscopy; imaging; segmentation; neural networks (information technology)

Free keywords: neural network; segmentation; cancer; in vitro; microscopy

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Ministry reporting: Yes

Reporting Year: 2020

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