  We present a dual-stage neural network architecture for analyzing fine shape
details from microscopy recordings in 3D. The system, tested on red blood
cells, uses training data from both healthy donors and patients with a
congenital blood disease. Characteristic shape features are revealed from the
spherical harmonics spectrum of each cell and are automatically processed to
create a reproducible and unbiased shape recognition and classification for
diagnostic and theragnostic use.
