Institute of Information Theory and Automation

Publication details

Automated Analysis of Microscopic Images of Isolated Pancreatic Islets

Journal Article

Habart D., Švihlík J., Schier Jan, Cahová M., Girman P., Zacharovová K., Berková Z., Kříž J., Fabryová E., Kosinová L., Papáčková Z., Kybic J., Saudek F.


serial: Cell Transplantation vol.25, 12 (2016), p. 2145-2156

project(s): GA14-10440S, GA ČR

keywords: enumeration of islets, image processing, image segmentation, islet transplantation, machine-learning, quality control

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abstract (eng):

Clinical islet transplantation programs rely on the capacities of individual centers to quantify isolated islets. We describe two machine learning algorithms for islet quantification, the trainable islet algorithm (TIA) and the non-trainable purity algorithm (NPA). These algorithms automatically segment pancreatic islets and exocrine tissue on microscopic images in order to count individual islets, and calculate islet volume and purity. References for islet counts and volumes were generated by the fully manual segmentation (FMS) method, which was validated against the internal DNA standard. References for islet purity were generated via the expert visual assessment (EVA) method, which was validated against the FMS method.

RIV: IN

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Last modification: 21.12.2012
Institute of Information Theory and Automation