Deep learning-based quality control of cultured human-induced pluripotent stem cell-derived cardiomyocytes
Using bright-field images of cultured human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), we trained a convolutional neural network (CNN), a machine learning technique, to decide whether the qualities of cell cultures are suitable for experiments. VGG16, an open-source CNN framew...
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| Main Authors: | , , , |
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| Format: | Book |
| Published: |
Elsevier,
2019-08-01T00:00:00Z.
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| Subjects: | |
| Online Access: | Connect to this object online. |
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| Summary: | Using bright-field images of cultured human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), we trained a convolutional neural network (CNN), a machine learning technique, to decide whether the qualities of cell cultures are suitable for experiments. VGG16, an open-source CNN framework, resulted in a mean F1 score of 0.89 and judged the cell qualities at a speed of approximately 2000 images per second when run on a commercially available laptop computer equipped with Core i7. Thus, CNNs provide a useful platform for the high-throughput quality control of hiPSC-CMs. Keywords: Machine learning, Heart, iPSC |
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| Item Description: | 1347-8613 10.1016/j.jphs.2019.04.008 |