The Jaccard similarity index, also known as the Tanimoto coefficient, measures the overlap of two sets. It is defined as the size of the intersection of the sets divided by the size of their union. In other words,
J ( A , B ) = | A ∩ B | / | A ∪ B |
where A and B are the two sets. This can also be expressed in terms of the true positive (TP), false positive (FP), and false negative (FN) sets as TP / (FP+TP+FN).
The Jaccard index is zero if the two sets are disjoint, i.e., they have no common members, and is one if they are identical. Higher numbers indicate better agreement in the sets, so when we apply this index to evaluate the agreement of brain segmentation segmentation results, the goal is to get as close to 1 as possible.
These numbers are provided in several pages on the results archive, which allows you to sort results by various metrics including Jaccard index.
