  We introduce a novel Deep Learning framework, which quantitatively estimates
image segmentation quality without the need for human inspection or labeling.
We refer to this method as a Quality Assurance Network -- QANet. Specifically,
given an image and a `proposed' corresponding segmentation, obtained by any
method including manual annotation, the QANet solves a regression problem in
order to estimate a predefined quality measure with respect to the unknown
ground truth. The QANet is by no means yet another segmentation method.
Instead, it performs a multi-level, multi-feature comparison of an
image-segmentation pair based on a unique network architecture, called the
RibCage.
  To demonstrate the strength of the QANet, we addressed the evaluation of
instance segmentation using two different datasets from different domains,
namely, high throughput live cell microscopy images from the Cell Segmentation
Benchmark and natural images of plants from the Leaf Segmentation Challenge.
While synthesized segmentations were used to train the QANet, it was tested on
segmentations obtained by publicly available methods that participated in the
different challenges. We show that the QANet accurately estimates the scores of
the evaluated segmentations with respect to the hidden ground truth, as
published by the challenges' organizers.
  The code is available at: TBD.
