Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin
Fig 1
Object categories in our images.
(A-C) The SCD pathogenetic pathway and changes undergone by the diseased RBC. A: A healthy RBC with biconcavity. The latter appears as a dimple viewed from the top. B (i-iii): Partially sickled sRBCs at increasing stages of sickling. The bi-concavity distends out to give a shallower dimple, and elongation in profile. This is the category we identify as deformable sRBC (see Section 1.3). B (iv-vi): Additional representative image variants of this category. C (i-iii): Highly sickled sRBCs. The dimple has completely disappeared and the shape is highly elongated. We classify these into our non-deformable category. C (iv-vi): More variants in the non-deformable category. Factors like local flow patterns, applied shear forces, and oxygen levels in the environment give rise to various shapes (teardrop, star-like, amorphous) for different sRBCs. D: White blood cells (WBCs). E: Non-functionally adhered objects. F: Other unclassified objects, like (i) platelet clusters, (ii-iii) lysed cells, (iv-v) dirt and dust. In our workflow types D-F are classified together in the non-sRBC category.