Figures
Classification of red blood cell shapes in flow using outlier tolerant machine learning
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
Citation: (2018) PLoS Computational Biology Issue Image | Vol. 14(6) June 2018. PLoS Comput Biol 14(6): ev14.i06. https://doi.org/10.1371/image.pcbi.v14.i06
Published: June 29, 2018
Copyright: © 2018 Quint. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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