  Large-scale image data such as digital whole-slide histology images pose a
challenging task at annotation software solutions. Today, a number of good
solutions with varying scopes exist. For cell annotation, however, we find that
many do not match the prerequisites for fast annotations. Especially in the
field of mitosis detection, it is assumed that detection accuracy could
significantly benefit from larger annotation databases that are currently
however very troublesome to produce. Further, multiple independent (blind)
expert labels are a big asset for such databases, yet there is currently no
tool for this kind of annotation available.
  To ease this tedious process of expert annotation and grading, we introduce
SlideRunner, an open source annotation and visualization tool for digital
histopathology, developed in close cooperation with two pathologists.
SlideRunner is capable of setting annotations like object centers (for e.g.
cells) as well as object boundaries (e.g. for tumor outlines). It provides
single-click annotations as well as a blind mode for multi-annotations, where
the expert is directly shown the microscopy image containing the cells that he
has not yet rated.
