Automated classification of bacterial cell sub-populations with convolutional neural networks
Fig 6
Comparing consistency and repeatability of manual cell counting.
(A) Standard deviation (STD) as a measure of variance of cell counts across 25 microscope images (ordered in increasing number of cells per image) using manual counting from five separate human counters (interoperator variability) benchmarked against the advanced rotation (AR) trained cCNN algorithm. Average variance of human subjects was 10.2 times larger than that of the cCNN algorithm (insert). (B) Standard deviation of each test subject when counting a duplicate image randomized in their set (intraoperator variability). Images 1 through 5 presented in the order of increasing cell population (Supplement 13 in S1 File). Total time required to count cells on all images for each operator is presented in the pie chart (inset). Full circle is 1000 seconds, tick marks are set to 100 seconds. Each individual is represented with a different color (Blue–Person 1; Orange–Person 2; Yellow–Person 3; Purple–Person 4; Green–Person 5). If the data from a specific person is not present, then their variability was 0 cells/image.