A software tool for studying the size and shape of human cardiomyocytes

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• 作者：Jyrki Rasku^a ; ^{jyrki.rasku@uta.fi" class="auth_mail" title="E-mail the corresponding author} ; Marisa Ojala^b ; Risto-Pekka Pö ; lö ; nen^b ; Henry Joutsijoki^a ; Yulia Gizatdinova^a ; Jorma Laurikkala^a ; Kimmo Kartasalo^b ; Katriina Aalto-Setä ; lä ; ^b ; ^c ; ^d ; Martti Juhola^a
• 关键词：Cardiomyocyte ; Segmentation ; Threshold
• 刊名：Biomedical Signal Processing and Control
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：30
• 期：Complete
• 页码：134-139
• 全文大小：1611 K

文摘

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.