Additional file 3: Figure S3. of On the representation of cells in bone marrow pathology by a scalar field: propagation through serial sections, co-localization and spatial interaction analysis

Direct spatial interaction in a case with a loose infiltration. Sections from a patient with CML were stained for CD3, CD20, CD4 and CD28. Slides were fully digitalized and registered like in the method section described. After segmentation there were 417 CD3+ and 6,187 CD3−/+ nuclei; 17 CD20+ and 5,770 CD20−/+ nuclei; 57 CD4+ and 6,394 CD4−/+ nuclei; and respectively 126 CD8+ and 5,696 CD8−/+ nuclei. A: Registered sections IHC-stained for CD3, CD20, CD4 and CD8. The registration between CD3 and CD20 and respectively between CD4 and CD8 is visually pretty good. However, due to different cuttings levels during processing, there is a continuous change of morphology. The white and the yellow arrow visualize these changes for one trabeculae and, respectively, for a focal lymphoid infiltrate. B: Scatter plot for all nuclei (CD3−/+, CD20−/+, CD4−/+ and CD8−/+). For CD3 vs. CD20 MCD3−/+ = 0.90 and MCD20−/+ = 0.90; for CD3 vs. CD4 MCD3−/+ = 0.90 and MCD4−/+ = 0.79; and respectively for CD4 vs. CD8 MCD4−/+ = 0.95 and MCD8−/+ = 0.80. C: Scatter plot for positive nuclei (CD3+, CD20+, CD4+ and CD8+). The respective overlap coefficients are shown in Table 1. (PNG 3007 kb

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

Additional file 1: Figure S1. of On the representation of cells in bone marrow pathology by a scalar field: propagation through serial sections, co-localization and spatial interaction analysis

Flow chart for the presented approach. The main steps are “preprocessing” in Fiji, “cell segmentation” in Matlab, “introduction of the RBF” in Matlab and “statistical evaluation” in Matlab and R. The preprocessing and the cell segmentation are performed by custom arranged standard methods like colour deconvolution, thresholding, cross correlation etc. (PNG 220 kb

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 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

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

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