Examples of digitally re-stained H&E images.

<p>(A-D) Normal lymph node tissue, (E-H) colorectal carcinoma tissue, (I-L) aspergillus, (M-P) breast cancer tissue surrounded by lymph node tissue. For each sample, the original image, the re-stained image and the original and resulting color map are shown. The color map used for re-staining was orange (#FFAD00)—blue (#006EFF). Sizes are: (A) 742 ∗ 742<i>μ</i>m, (E) 742 ∗ 742<i>μ</i>m, (N) 594 ∗ 594<i>μ</i>m. (I) had no specified size (image source see List B in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145572#pone.0145572.s004" target="_blank">S1 File</a>).</p

An optimized approach for color deconvolution based on principal component analysis minimizes the deconvolution residual.

<p>A) the image pixels of a given blue—brown immunostained image are plotted in the space of optical density color channels (OD R = red, OD G = green, OD B = blue). The commonly used standard color deconvolution vectors define a plane that roughly, but not optimally, approximates the data set (darker plane). The brighter plane shows the optimal plane containing the first and the second principal component vector of the actual image pixel dataset. B) Quantification of mean square error of the residual channel after color deconvolution with different deconvolution vectors. Boxes = 25th to 75th percentile, line = median, whiskers = most extreme data points except outliers, ‘+’ = outliers.</p

Foreground-background contrast in phantom images can be markedly increased by applying a new color map.

<p>A-F) A phantom image was colored in one of six bivariate color maps, the panel title indicate the hexadecimal codes of the foreground and background color (F represents a typical blue—brown standard color map while A-E represent new color combinations). G) 225 combinations of 15 distinct colors were pairwisely compared. The grayscale intensity and overlayed number indicate the perceptual contrast of phantom images that were digitally stained with the respective color map (numbers: mean ± standard deviation). All measurements were normalized to the maximum contrast. Reading example: The standard brown (#B58C70) on blue (#5C5FA1) color map resulted in a perceptual contrast that was 39% ± 7% of the maximally achieved contrast.</p

Digital re-staining markedly increases perceptual contrast in N = 596 actual histological images.

<p>Perceptual contrast before (gray boxes) and after (black boxes) color map improvement was measured in eight sets of histological images of human solid tumors (see ‘Materials and Methods’, total N = 596 samples). For all datasets, a pronounced increase of perceptual contrast can be seen. Boxes = 25th to 75th percentile, line = median, whiskers = most extreme data points except outliers, ‘+’ = outliers. The color map used for re-staining was blue (#006EFF)—orange (#FFAD00).</p

Digitally re-stained images of Ki67 stained samples.

<p>Two representative images from set ‘MKI67-uro’ (Ki67 in urothelial cancer). (A, C): original images, (B, D): re-stained images in red (#FF0000)—blue (#0093FF). It can be seen that the contrast of foreground (i.e. Ki67 positive cells) to background is improved after re-staining.</p

New color maps offer higher perceptual contrast than original color map in all relevant color map regions.

<p>A) Original image. B) Image re-stained with the optimized red (#FF0000)—blue (#0093FF) color map. C) Image re-stained with the optimized blue (#006EFF)—orange (#FFAD00) color map. A.1, B.1, C.1) Corresponding bivariate color maps. A.2, B.2, C.2) Intensity represents perceptual contrast (CIELAB distance) of A.1, B.2, C.1 relative to the center point. It can be seen that perceptual contrast of the original color map is low in almost all regions while the perceptual contrast of the new color maps is much higher.</p

Measuring the bivariate color map in a given image.

<p>A) Original image detail (CD31 staining of tumor tissue), B) image pixels plotted in the space of the basis vectors of the two stain colors (H—DAB intensity space). C) Original bivariate color map interpolated from B. This color map contains all colors that are part of the original image. They are arranged in the H—DAB intensity space.</p

New color maps contain more colors than the original color map.

<p>In this figure, bivariate color maps are compared to the full sRGB gamut in CIELAB color space. A) Original color map extracted from the sample image. B) Set of 5 optimized color maps in the same view (color maps from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145572#pone.0145572.g004" target="_blank">Fig 4A–4E</a>). It can be seen that the improved color maps occupy a larger part of the perceivable color space, maximizing visually transmittable information.</p

Identification of a characteristic vascular belt zone in human colorectal cancer

<div><p>Blood vessels in cancer</p><p>Intra-tumoral blood vessels are of supreme importance for tumor growth, metastasis and therapy. Yet, little is known about spatial distribution patterns of these vessels. Most experimental or theoretical tumor models implicitly assume that blood vessels are equally abundant in different parts of the tumor, which has far-reaching implications for chemotherapy and tumor metabolism. In contrast, based on histological observations, we hypothesized that blood vessels follow specific spatial distribution patterns in colorectal cancer tissue. We developed and applied a novel computational approach to identify spatial patterns of angiogenesis in histological whole-slide images of human colorectal cancer.</p><p>A characteristic spatial pattern of blood vessels in colorectal cancer</p><p>In 33 of 34 (97%) colorectal cancer primary tumors blood vessels were significantly aggregated in a sharply limited belt-like zone at the interface of tumor tissue to the intestinal lumen. In contrast, in 11 of 11 (100%) colorectal cancer liver metastases, a similar hypervascularized zone could be found at the boundary to surrounding liver tissue. Also, in an independent validation cohort, we found this vascular belt zone: 22 of 23 (96%) samples of primary tumors and 15 of 16 (94%) samples of liver metastases exhibited the above-mentioned spatial distribution.</p><p>Summary and implications</p><p>We report consistent spatial patterns of tumor vascularization that may have far-reaching implications for models of drug distribution, tumor metabolism and tumor growth: luminal hypervascularization in colorectal cancer primary tumors is a previously overlooked feature of cancer tissue. In colorectal cancer liver metastases, we describe a corresponding pattern at the invasive margin. These findings add another puzzle piece to the complex concept of tumor heterogeneity.</p></div

Histological aspect of the vascular belt zone in CRC tissue.

<p>CRC primary tumor sample stained for CD34 (brown), (A) primary tumor near the invasion front contains few blood vessels, (B) liver metastasis near the invasion front contains many small blood vessels, (C) primary tumor at the intestinal lumen contains many dilated blood vessels, (D) liver metastasis tumor center contains few blood vessels.</p