%0 Figure %A Takagi, Yuko %A Nosato, Hirokazu %A Doi, Motomichi %A Furukawa, Koji %A Sakanashi, Hidenori %D 2019 %T Supplementary Figure 1. Development of a motion-based cell-counting system for Trypanosoma parasite using a pattern recognition approach %U https://future-science-group.figshare.com/articles/figure/Supplementary_Figure_1_Development_of_a_motion-based_cell-counting_system_for_Trypanosoma_parasite_using_a_pattern_recognition_approach/7873283 %R 10.25402/BTN.7873283.v1 %2 https://future-science-group.figshare.com/ndownloader/files/14659031 %K cell count %K Chagas disease %K image analysis %K machine learning %K microscopy pattern recognition %K protozoan parasite %K Trypanosoma cruzi %K Cell Development, Proliferation and Death %K Medical Parasitology %K Host-Parasite Interactions %X
Supplemental Figure1.
T. cruzi epimastigote cell count estimation by conventional methods. (A)TC20automated cell counter(Bio-Rad, Hercules, California, USA)was used to count rosette-rich epimastigote, according to the manufacturer’s instruction. Actual cell count was determined by using hemocytometer after breaking apart cell aggregates by pipetting and vortexing. Dottedlinerepresentsthetheoreticallineofperfectcorrelationbetweenactual cell counts and machine-estimated cell counts. (B)Fiji-ImageJ software[1] was used to count the same sample as(A),using threshold adjustment and watershed segmentation functions.(C)Cell Profiler Worm Toolbox[2] was used to count the same sample as(A).The same still images used in (B)were analyzed.

%I Future Science Group