0465945 20240111140928.920161124235959.9 85007086435 000390183200005 10.3727/096368916X692005 12 s. P cav_un_epca*02523930963-68972512 (2016)2145-2156Sage enumeration of islets image processing image segmentation islet transplantation machine-learning quality control cav_un_auth*0336694 Habart D. CZ cav_un_auth*0277057 Švihlík J. CZ cav_un_auth*0101190 Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Schier Jan UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0216102 Cahová M. CZ cav_un_auth*0221384 Girman P. CZ cav_un_auth*0221382 Zacharovová K. CZ cav_un_auth*0221383 Berková Z. CZ cav_un_auth*0214394 Kříž J. CZ cav_un_auth*0307772 Fabryová E. CZ cav_un_auth*0284136 Kosinová L. CZ cav_un_auth*0320486 Papáčková Z. CZ cav_un_auth*0233560 Kybic J. CZ cav_un_auth*0069778 Saudek F. CZ PDF dokument http://library.utia.cas.cz/separaty/2016/ZOI/schier-0465945.pdf cav_un_auth*0339800 GA14-10440S GA ČR CZ 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. IN 2017 13 4 A hod 4ah 20231122142036.8 RVO:67985556 http://hdl.handle.net/11104/0265686 cav_un_auth*0336695 Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague ČVUT FEL CZ cav_un_auth*0336696 Diabetes Center, Institute for Clinical and Experimental Medicine IKEM CZ cav_un_auth*0331984 University of Chemistry and Technology Prague CZ cav_un_auth*0336697 Institute of Information Theory and Automation of the CAS ÚTIA AV ČR CZ cav_un_auth*0336698 Center of Experimental Medicine, Institute for Clinical and Experimental Medicine IKEM CZ 1 Department of Image Processing UTIA-B 10200 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS S 3+4 Article Cell Tissue Engineering|Medicine Research Experimental|Transplantation CELL&TISSUEENGINEERING|TRANSPLANTATION|MEDICINE.RESEARCH&EXPERIMENTAL 3.121 0.539 4.8 0.00996 0.769 4703 1.063 Q1 2.852 167 Q2 59.92 52.4 Q2 Q3 66.797 2016 Soubory v repozitáři: schier-0465945.pdf 85007086435 SCOPUS 000390183200005 WOS PDF dokument cav_un_epca*0252393 Cell Transplantation 0963-6897 1555-3892 Roč. 25 č. 12 2016 2145 2156 Sage