Classification of red blood cell shapes in flow using outlier tolerant machine learning

The manual evaluation, classification and counting of biological objects demands for an enormous expenditure of time and subjective human input may be a source of error. Investigating the shape of red blood cells (RBCs) in microcapillary Poiseuille flow, we overcome this drawback by introducing a convolutional neural regression network for an automatic, outlier tolerant shape classification. From our experiments we expect two stable geometries: the so-called ‘slipper’ and ‘croissant’ shapes depending on the prevailing flow conditions and the cell-intrinsic parameters. Whereas croissants mostly occur at low shear rates, slippers evolve at higher flow velocities. With our method, we are able to find the transition point between both ‘phases’ of stable shapes which is of high interest to ensuing theoretical studies and numerical simulations. Using statistically based thresholds, from our data, we obtain so-called phase diagrams which are compared to manual evaluations. Prospectively, our concept allows us to perform objective analyses of measurements for a variety of flow conditions and to receive comparable results. Moreover, the proposed procedure enables unbiased studies on the influence of drugs on flow properties of single RBCs and the resulting macroscopic change of the flow behavior of whole blood.Author summary: Artificial neural networks represent a state-of-the art technique in many branches of natural sciences due to their ability to fastly detect and categorize image features with high throughput. We use a special type of neural network, the so-called convolutional neural network (CNN) for the classification of human red blood cell shapes in microcapillary Poiseuille flow. Following this approach, phase diagrams of two distinct classes (slippers, croissants) are generated and, by comparison with a manually obtained phase diagram, optimized threshold ranges for categorizing the output values are established. This allows us to better understand the complex fluid behavior of blood depending on the intrinsic properties of single red blood cells. For future studies, we aim to predict phase diagrams under the influence of certain drugs.