  Quantifying the accuracy of segmentation and manual delineation of organs,
tissue types and tumors in medical images is a necessary measurement that
suffers from multiple problems. One major shortcoming of all accuracy measures
is that they neglect the anatomical significance or relevance of different
zones within a given segment. Hence, existing accuracy metrics measure the
overlap of a given segment with a ground-truth without any anatomical
discrimination inside the segment. For instance, if we understand the rectal
wall or urethral sphincter as anatomical zones, then current accuracy measures
ignore their significance when they are applied to assess the quality of the
prostate gland segments. In this paper, we propose an anatomy-aware measurement
scheme for segmentation accuracy of medical images. The idea is to create a
``master gold'' based on a consensus shape containing not just the outline of
the segment but also the outlines of the internal zones if existent or
relevant. To apply this new approach to accuracy measurement, we introduce the
anatomy-aware extensions of both Dice coefficient and Jaccard index and
investigate their effect using 500 synthetic prostate ultrasound images with 20
different segments for each image. We show that through anatomy-sensitive
calculation of segmentation accuracy, namely by considering relevant anatomical
zones, not only the measurement of individual users can change but also the
ranking of users' segmentation skills may require reordering.
