A software tool for studying the size and shape of human cardiomyocytes. (September 2016)

• Record Type: Journal Article
• Authors: Rasku, Jyrki; Ojala, Marisa; Pölönen, Risto-Pekka; Joutsijoki, Henry; Gizatdinova, Yulia; Laurikkala, Jorma; Kartasalo, Kimmo; Aalto-Setälä, Katriina; Juhola, Martti
• Is Part Of: Biomedical signal processing and control. Volume 30(2016)
• Page Start: 134
• Page End: 139
• Publication Date: 2016-09
• Subjects: Cardiomyocyte -- Segmentation -- Threshold; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.06.011
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Background and objectives: Due to development of imaging systems the amount of digital images obtained in the biological field has been growing in recent years. These images contain information that is not directly measurable, e.g. the area covered by a single cell. In most of the current imaging programs the regions of interest (ROI), e.g. individual cells, need to be manually outlined. Automation of processing and analyzing the images would ease researchers' workload and provide results that are more reliable. In this work our goal was to write software that automatically segments human cardiomyocytes from images, calculates their areas and variations in the direction of the largest and smallest spread. Results: We developed software that eased the workload of biomedical laboratory personnel such that they do not have to do manual image segmentation or learn to use software that requires programming skills. The software made a correct segmentation in most of the cases and outperformed the intensity oriented baseline method written in ImageJ in 95% of comparisons. The baseline method estimated cell- and background areas by averaging dark background and bright foreground areas. Conclusions: Our software can be used in the calculation of cell areas and extents in the case where immunolabeled cells are imaged with a fluorescent microscope. In the future the functionality of the program could be extended with machine learning methods that use the user actions as teaching material in the cases where automatic segmentation fails.