Abstract:
Context. Whole-slide imaging has ushered in a new era of technology that has
fostered the use of computational image analysis for diagnostic support and
has begun to transfer the act of analyzing a slide to computer monitors. Due
to the overwhelming amount of detail available in whole-slide images,
analytic procedures - whether computational or visual - often operate at
magnifications lower than the magnification at which the image was acquired.
As a result, a corresponding reduction in image resolution occurs. It is
unclear how much information is lost when magnification is reduced, and
whether the rich color attributes of histologic slides can aid in
reconstructing some of that information.
Objective. To examine the correspondence between the color and spatial
properties of whole-slide images to elucidate the impact of resolution
reduction on the histologic attributes of the slide.
Design. We simulated image resolution reduction and
modeled its effect on classification of the underlying histologic structure.
By harnessing measured histologic features and the intrinsic spatial
relationships between histologic structures, we developed a predictive
model to estimate the histologic composition of tissue in a manner that
exceeds the resolution of the image.
Results. Reduction in resolution resulted in a significant loss of the
ability to accurately characterize histologic components at magnifications
less than ×10. By utilizing pixel color, this ability was improved at all
magnifications.
Conclusions. Multiscale analysis of histologic images requires an adequate
understanding of the limitations imposed by image resolution. Our findings
suggest that some of these limitations may be overcome with computational
modeling.
