We investigate the shape transitions of red blood cells flowing in micro-capillaries. A convolutional neural network (CNN) transforms bright-field images of cells into a set of abstract representations. The illustration shows different shape features of the same cell highlighted by the color scheme and dependent on the applied convolution kernel. Our approach allows for a quantitative classification of each recorded cell. According to the prevailing flow conditions, the CNN reveals the formation of two stable shapes, "slippers" and "croissants". Finally, a phase diagram assessed within a range of physiolocial relevant shear rates expresses the shape transition in between these states. Kihm et al.
Image Credit: Stephan Quint
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2018 ISCB Innovator Award recognizes M. Madan Babu
PLOS Computational Biology: published June 7, 2018 | https://doi.org/10.1371/journal.pcbi.1006164
2018 ISCB Overton Prize awarded to Cole Trapnell
PLOS Computational Biology: published June 7, 2018 | https://doi.org/10.1371/journal.pcbi.1006163
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