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Marking tumors and organs is a challenging task suffering from both inter-and intra-observer variability. The literature quantifies observer variabilityby generating consensus among multiple experts when they mark the same image.Automatically building consensus contours to establish quality assurance forimage segmentation is presently absent in the clinical practice. As the\emph{big data} becomes more and more available, techniques to access a largenumber of existing segments of multiple experts becomes possible. Fastalgorithms are, hence, required to facilitate the search for similar cases. Thepresent work puts forward a potential framework that tested with small datasets(both synthetic and real images) displays the reliability of finding similarimages. In this paper, the idea of content-based barcodes is used to retrievesimilar cases in order to build consensus contours in medical imagesegmentation. This approach may be regarded as an extension of the conventionalatlas-based segmentation that generally works with rather small atlases due torequired computational expenses. The fast segment-retrieval process viabarcodes makes it possible to create and use large atlases, something thatdirectly contributes to the quality of the consensus building. Because theaccuracy of experts' contours must be measured, we first used 500 syntheticprostate images with their gold markers and delineations by 20 simulated users.The fast barcode-guided computed consensus delivered an average error of$8\%\!\pm\!5\%$ compared against the gold standard segments. Furthermore, weused magnetic resonance images of prostates from 15 patients delineated by 5oncologists and selected the best delineations to serve as the gold-standardsegments. The proposed barcode atlas achieved a Jaccard overlap of$87\%\!\pm\!9\%$ with the contours of the gold-standard segments.

Fast Barcode Retrieval for Consensus Contouring