Combining assembles of domain expert markings

Abstract: Breast cancer is diagnosed in more than 6300 Swedish women every year. Mammograms, which are X-ray images of breasts, are taken as part of a nationwide screening process and are analyzed for anomalies by radiologists. This analysis process could be made more efficient by using computer-aided image analysis to assist quality control of the mammograms. However, the development of such image analysis methods requires what is called a “ground truth”. The ground truth is used as a key in algorithm development and represents the true information in the depicted object. Mammograms are 2D projections of deformed 3D objects, and in these cases the ground truth is almost impossible to procure. Instead a surrogate ground truth is constructed. ALGSII, a novel method for ranking shapes within a given set, was recently developed for measuring the level of agreement among ensembles of markings produced by experts of glandular tissue in mammograms. It was hypothesized in this thesis that the ALGSII measure could be used to construct a surrogate truth based on the markings from domain experts.Markings from segmentations of glandular tissue, performed by 5 different field experts on 162 mammograms, comprised the working data for this thesis project. An algorithm was developed that, given a fixed set of markings, takes an initial shape and modifies it iteratively until it becomes the “optimal shape” - the shape with the highest level of agreement in the group of markings according to the ALGSII measure. The algorithm was optimized with egard to rate of accepted shape changes and computational complexity.The developed algorithm was successful in producing an optimal shape according to the definition of maximizing the ALGSII measure in 100% of the cases tested. The algorithm showed stability for the given data set, and its performance was significantly increased by the implemented optimizations.