Skip to main content

Table 3 Quantitative comparison of segmentation accuracy between CELLPROFILER and CELLSEGM as measured by falsely fused, splitted, positive and negative cells for all four data sets

From: CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation

Comparison/ Falsely fused Falsely splitted False positive False negative
O1-CellSegm 15 13 1 1
O2-CellSegm 6 11 2 8
O1-CellProfiler 72 4 0 49
O2-CellProfiler 62 0 0 56
  1. CELLPROFILER has much more falsely fused cells and false negative cells than CELLSEGM, although CELLSEGM has more falsely splitted cells. The latter is mostly due to cells having two nuclei, which is not uncommon for cancer cell lines. The false positive and false negative rates are very low for CELLSEGM. The robustness of CELLSEGM is greatly improved when the nucleus approach is applied, where a cell is initiated only if a valid nucleus marker exists.