  One of the challenges facing the adoption of digital pathology workflows for
clinical use is the need for automated quality control. As the scanners
sometimes determine focus inaccurately, the resultant image blur deteriorates
the scanned slide to the point of being unusable. Also, the scanned slide
images tend to be extremely large when scanned at greater or equal 20X image
resolution. Hence, for digital pathology to be clinically useful, it is
necessary to use computational tools to quickly and accurately quantify the
image focus quality and determine whether an image needs to be re-scanned. We
propose a no-reference focus quality assessment metric specifically for digital
pathology images, that operates by using a sum of even-derivative filter bases
to synthesize a human visual system-like kernel, which is modeled as the
inverse of the lens' point spread function. This kernel is then applied to a
digital pathology image to modify high-frequency image information deteriorated
by the scanner's optics and quantify the focus quality at the patch level. We
show in several experiments that our method correlates better with ground-truth
$z$-level data than other methods, and is more computationally efficient. We
also extend our method to generate a local slide-level focus quality heatmap,
which can be used for automated slide quality control, and demonstrate the
utility of our method for clinical scan quality control by comparison with
subjective slide quality scores.
