1. A method for determining the quality of cell segmentation of a marker stained biological specimen, comprising: obtaining training image feature data and selecting training image features for a plurality of cells in a training set stained with a plurality of segmentation markers, the plurality of cells comprises at least one cell with “good” segmentation as annotated by an expert, the at least one cell having a training image feature vector defined by the training image features, creating a training image feature space comprising a hyper-sphere with a spherical boundary, wherein a dimension of the training image feature space is determined by a dimension of the training image features and the size of the hyper-sphere is defined by its volume in the feature space, wherein the size of the hyper-sphere is adjustably modified so that the training image feature vector of the at least one cell with “good” segmentation is inside or on the boundary of the hyper-sphere; selecting test image features and extracting test image feature data for a test cell stained with at least one segmentation markers in the marker stained biological specimen, wherein the test image features are selected to be the same as the training image features, and the test cell has a test image feature vector defined by the test image features; applying the hyper-sphere to the test image feature vector and determining whether the test image feature vector of the at least one test cell is on the boundary of, inside or outside the hyper-sphere of the training image feature space, the test image feature vector being on the boundary of or inside the hyper-sphere indicating that the at least one test cell includes “good” segmentation and the test image feature vector being outside the hyper-sphere indicating that the at least one segmentation marker stained test cell includes “poor” segmentation.