1. A method for determining the quality of cell segmentation of a marker stained biological specimen, comprising: building a training model including: obtaining training image feature data for a plurality of segmentation marker stained training cells, the training image feature data including a plurality of cell features for each cell in the plurality of segmentation marker stained training cells, examining visually by an expert each cell in the plurality of segmentation marker stained training cells to determine which cells in the plurality of segmentation marker stained training cells include “good” segmentation, isolating the training image feature data for each cell included in the plurality of segmentation marker stained training cells that include “good” segmentation, creating an training image feature vector from the training image feature data for each cell included in the plurality of segmentation marker stained training cells that include “good” segmentation, the number of dimensions of the training image feature vector depending on the number of cell features in the plurality of cell features for each cell in the plurality of segmentation marker stained training cells, and creating the training model using the training image feature vector for each cell included in the plurality of segmentation marker stained cells that include “good” segmentation to define a hyper-sphere such that a cell with an image vector that is on the boundary of or inside the hyper-sphere includes “good” segmentation and a cell with an image vector that is outside the hyper-sphere includes “poor” segmentation; obtaining test image feature data for at least one segmentation marker stained test cell from the marker stained biological specimen, the test image feature data including a plurality of cell features for the at least one segmentation marker stained test cell; isolating the test image feature data for the at least one segmentation marker stained test cell; creating an test image feature vector from the test image feature data for the at least one segmentation marker stained test cell, the number of dimensions of the test image feature vector for the at least one segmentation marker stained test cell depending on the number of cell features in the plurality of cell features for the at least one segmentation marker stained test cell; and determining whether the test image feature vector of the at least one segmentation marker stained test cell is on the boundary of, inside or outside the hyper-sphere of the training model, the image vector being on the boundary of or inside the hyper-sphere indicating that the at least one segmentation marker stained test cell includes “good” segmentation and the image vector being outside the hyper-sphere indicating that the at least one segmentation marker stained test cell includes “poor” segmentation.