An Image Analysis Resource for Cancer Research: PIIP—Pathology Image Informatics Platform for Visualization, Analysis, and Management

Abstract

Pathology Image Informatics Platform (PIIP) is an NCI/NIH sponsored project intended for managing, annotating, sharing, and quantitatively analyzing digital pathology imaging data. It expands on an existing, freely available pathology image viewer, Sedeen. The goal of this project is to develop and embed some commonly used image analysis applications into the Sedeen viewer to create a freely available resource for the digital pathology and cancer research communities. Thus far, new plugins have been developed and incorporated into the platform for out of focus detection, region of interest transformation, and IHC slide analysis. Our biomarker quantification and nuclear segmentation algorithms, written in MATLAB, have also been integrated into the viewer. This paper describes the viewing software and the mechanism to extend functionality by plugins, brief descriptions of which are provided as examples, to guide users who want to utilize this platform.

Serum‐derived carcinoembryonic antigen (CEA) activates fibroblasts to induce a local re‐modeling of the extracellular matrix that favors the engraftment of CEA‐expressing tumor cells

“…For the quantification of DAB staining patterns observed by IHC, digitized images from scanned slides were analyzed using the Sedeen Viewer v5.0.3 (beta; Pathcore Inc, Toronto, Ontario, Canada) . Images corresponding to specimens stained using the anti‐CEA, Fn and α‐SMA antibodies were displayed side by side.…”

“…Images corresponding to specimens stained using the anti‐CEA, Fn and α‐SMA antibodies were displayed side by side. Using the manual alignment tools in the viewer, all three sets of images were aligned and regions of interest were defined manually on each image . Image processing and stain analysis were carried out using a software plug‐in written using the Sedeen SDK provided by Pathcore.…”

“…Image processing and stain analysis were carried out using a software plug‐in written using the Sedeen SDK provided by Pathcore. First, color deconvolution was performed to isolate the DAB stain (brown color) corresponding to the IHC marker from the digitized images as previously described . A threshold was then determined by visual assessment; it was found that a threshold of 1.0 (where each stain image had been rescaled to the range 0.0–255.0) provided a good separation between stained nuclei and unstained background regions, with the stained nuclei corresponding to pixels with an intensity ≤1.0.…”

“…A threshold was then determined by visual assessment; it was found that a threshold of 1.0 (where each stain image had been rescaled to the range 0.0–255.0) provided a good separation between stained nuclei and unstained background regions, with the stained nuclei corresponding to pixels with an intensity ≤1.0. The ratio of the stained area to the total area was then calculated for each region of interest and each slide and was expressed as a percentage . The analysis was performed on the full resolution images without down sampling.…”

Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline

“…Archiving and indexing large datasets is a nontrivial, but a possible task, as demonstrated by the plethora of database repositories currently in use for biology research, electron microscopy, microarray analysis, radiology, or multidiscipline research …”