Abstract
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.
Highlights
Amongst all human organs, blood is the most delocalized one, delivering oxygen from the respiratory system to the tissues in the body and transporting carbon dioxide back
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
Phase diagrams of two distinct classes are generated and, by comparison with a manually obtained phase diagram, optimized threshold ranges for categorizing the output values are established
Summary
Blood is the most delocalized one, delivering oxygen from the respiratory system to the tissues in the body and transporting carbon dioxide back. RBCs are biconcave discocytes with an average diameter of 8 μm and a height of 2 μm. Due to their flexible membrane, RBCs alter their shape under external stress prevalent in the microvascular network [1, 2]. This feature is one of the key properties of RBCs, which allows them to squeeze through geometrical constrictions much smaller than their stress-free shape [3], which is partly an intrinsic property of RBC morphology [4, 5] and partly an active adaptation process [6, 7]. The experimental data originate from a previous study on RBC shape geometry [10] and the data is reused for the introduction of a fully automated data analysis approach based on a deep learning convolutional neural network (CNN) [11]
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