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

• Published in: PLoS computational biology Vol. 14; no. 6; p. e1006278
• Main Authors: Kihm, Alexander, Kaestner, Lars, Wagner, Christian, Quint, Stephan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 15.06.2018; Public Library of Science (PLoS)
• Subjects: Artificial intelligence; Biology and Life Sciences; Blood; Blood cells; Classification; Computer and Information Sciences; Computer simulation; Engineering and Technology; Error analysis; Erythrocytes; Flow; Flow cytometry; Footwear; Funding; International conferences; Laminar flow; Learning algorithms; Machine learning; Medicine and Health Sciences; Methods; Neural networks; Phase diagrams; Phase transitions; Physics; Physiological aspects; Red blood cells; Regression analysis; Research and Analysis Methods; Supervision; Technology application; Luxembourg; Germany

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 co...