The detection accuracy measure (DET) assesses how accurately each given object has been identified by comparing the set of objects computed by an evaluated algorithm with the reference set of objects given by the gold tracking truth. Numerically, DET is defined as a normalized Acyclic Oriented Graph Matching (AOGM-D) measure for detection:
DET = 1- min(AOGM-D, AOGM-D0)/AOGM-D0
where AOGM-D is the cost of transforming the computed set of nodes into the reference one, and AOGM-D0 is the cost of creating the reference set of nodes from scratch (i.e., it is AOGM-D for empty detection results). The minimum operator in the numerator prevents the DET value from being negative in the case when it is cheaper to create the reference set of nodes from scratch than to transform the computed set of nodes into the reference one. The normalization ensures that DET always falls in the [0,1] interval, with higher values corresponding to better detection performance.
The segmentation accuracy measure (SEG) assesses how well the segmented regions match the actual cell or nucleus boundaries by comparing the cell instance segmentation masks computed by an evaluated algorithm with the reference masks given by the gold segmentation truth. Numerically, SEG is based on the Jaccard similarity index as detailed in 
SEG.pdf, and falls in the [0,1] interval, with higher values corresponding to better segmentation performance.
The tracking accuracy measure (TRA) assesses how accurately each given object has been identified and followed in successive frames by comparing the acyclic oriented graph computed by an evaluated algorithm with the reference graph given by the gold tracking truth. Numerically, TRA is defined as a normalized Acyclic Oriented Graph Matching (AOGM) measure:
TRA = 1- min(AOGM, AOGM0)/AOGM0
where AOGM0 is the AOGM value required for creating the reference graph from scratch (i.e., it is the AOGM value for empty tracking results). The minimum operator in the numerator prevents the TRA value from being negative in the case when it is cheaper to create the reference graph from scratch than to transform the computed graph into the reference graph. The normalization ensures that TRA always falls in the [0,1] interval, with higher values corresponding to better tracking performance.
Command-line software packages that implement the DET, SEG and TRA measures are made publicly available, along with the instructions required to run the packages. These packages are used for the official evaluation of the algorithms by the challenge organizers and can be used by the participants to evaluate and tune their algorithms too.
To allow a direct comparison of the algorithms included in the Cell Segmentation Benchmark, the overall performance measure (OPCSB) is computed by averaging the corresponding DET and SEG values:
OPCSB = 0.5⋅(DET + SEG).
To allow a direct comparison of the algorithms included in the Cell Tracking Benchmark, the overall performance measure (OPCTB) is computed by averaging the corresponding SEG and TRA values:
OPCTB = 0.5⋅(SEG + TRA).