  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.
