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Table 1 Algorithmic performance of LoS, FARSight, 3D Watershed and ilastik

From: Robust and automated three-dimensional segmentation of densely packed cell nuclei in different biological specimens with Lines-of-Sight decomposition

Dataset # cells GT Algorithm # cells Seg Match Recall Precision Accuracy F-measure
Mouse embryo 61 LoS 59 58 0.95 0.98 0.94 0.97
FARSight 62 60 0.98 0.97 0.95 0.98
3D Watershed 299 61 1.00 0.20 0.20 0.34
ilastik 274 61 1.00 0.22 0.22 0.36
Breast cancer spheroid 240 LoS 247 216 0.90 0.87 0.80 0.89
FARSight 338 236 0.98 0.70 0.69 0.82
3D Watershed 288 220 0.92 0.76 0.71 0.83
ilastik 112 74 0.31 0.66 0.27 0.42
Pancreatic cancer spheroid 531 LoS 690 523 0.98 0.76 0.75 0.86
FARSight 997 524 0.99 0.53 0.52 0.69
3D Watershed 734 518 0.98 0.70 0.69 0.81
ilastik 19744 531 1.00 0.03 0.03 0.05
  1. Performance was measured against manually segmented ground truth for the three different test datasets. “# cells GT”, “# cells Seg” and “Match” list the number of cells that were determined manually in the ground truth, segmented by the different algorithms, and matched, respectively. The segmentation performance is given in terms of the metrics “Recall”, “Precision”, “Accuracy” and “F-measure”. Thereby, values range from 0 (worst performance) to 1 (best performance). For the LoS algorithm the same set of parameter values was used for all test images. For 3D Watershed and FARSight different parameter sets had to be used. These were determined by parameter scanning