A Methodology for Quality Control in Cell Nucleus Segmentation

In order to achieve the very high accuracy rates required in unsupervised automated biomedical applications, it is often necessary to complement a successful segmentation algorithm with a robust error checking stage. The better the segmentation strategy, the less severe the error checking decisions need to be and the fewer correct segmentations that are discarded. These issues are dealt with in this paper in order to achieve 100% accuracy on a data set of 19946 cell nucleus images using an established segmentation scheme with a success rate of 99.47%. The method is based upon measuring changes in the final segmentation contour as the one parameter that governs its behaviour is varied.