Date Published: March 29, 2017
Publisher: Public Library of Science
Author(s): Mark D. Zarella, Chan Yeoh, David E. Breen, Fernando U. Garcia, Abhijit De.
Digital imaging of H&E stained slides has enabled the application of image processing to support pathology workflows. Potential applications include computer-aided diagnostics, advanced quantification tools, and innovative visualization platforms. However, the intrinsic variability of biological tissue and the vast differences in tissue preparation protocols often lead to significant image variability that can hamper the effectiveness of these computational tools. We developed an alternative representation for H&E images that operates within a space that is more amenable to many of these image processing tools. The algorithm to derive this representation operates by exploiting the correlation between color and the spatial properties of the biological structures present in most H&E images. In this way, images are transformed into a structure-centric space in which images are segregated into tissue structure channels. We demonstrate that this framework can be extended to achieve color normalization, effectively reducing inter-slide variability.
Hematoxylin and Eosin (H&E) staining variability is ubiquitous in pathology, and has significant consequences on the digital pathology workflow. Image analytics that can potentially serve an important role in computer-aided diagnostics may become compromised in the presence of high variability. In particular, algorithms that rely on pixel color or intensity to characterize tissue in an automated fashion (e.g. [1–5]) may benefit from increased color homogeneity in the image samples. Color standardization therefore becomes an important preprocessing step for many computational algorithms . Likewise, methods to ensure laboratory compliance with staining protocols should be developed to ensure high quality patient care .
We developed a multi-stage algorithm, designed to operate with a minimal number of free parameters, to rapidly transform H&E images into a space more amenable to image processing and quantification with a primary goal of mitigating staining variability across images. This processing framework is divided into four main steps: 1) clustering by color to reduce the complexity of the image into a tractable set of channels; 2) assigning individual color channels to tissue elements to establish anchor points for calibration; 3) applying machine learning to parse the entire image into the assigned tissue elements; 4) transforming the classified image into a standardized space. This standardized space can include, for example, a nuclear likelihood space to aid in nuclear segmentation. However, we approach this report from the standpoint of color normalization, and therefore the standardized space we focus on is defined by a target that will enable more homogeneous visualization across images.
H&E staining variability poses a problem that is difficult to overcome in many applications. The sources of variability are present in nearly every step of the preparation and also include the inherent biological diversity of tissues . To reduce variability, standardization at a number of different stages should be implemented, potentially impacting tissue preparation techniques, staining protocols, microscopy standards, and the digitization process. However, retrospective data analysis does not benefit from any of these proposed improvements, and recalibration to new standards would become necessary every time a change is adopted. We have developed an image processing approach to achieving color standardization by utilizing the rich spatial information in H&E images. This approach provides a computationally realizable solution to standardization, accommodates retrospective data analysis, and is adaptable to new protocols.