color standardization for the …black [13 - 15] can be observed, as in figures in this paper. to...

Color standardization for the immunohistochemically stained tissue section images

Anna Korzynska, Jakub Zak, Lukasz Roszkowiak, Dorota Pijanowska

Laboratory of Processing Systems of Microscopic Image Information

Nalecz IBBE PAS Warsaw, Poland

[email protected]

Tomasz Markiewicz Military Institute of Medicine, Szaserow 128, 04-141

Warsaw, Poland Warsaw University of Technology, pl. Politechniki 1,

Warsaw, Poland

Abstract — Refining the information about the tissue is achieved by the standardization of color in whole-slide images and digital images of tissue sections. The investigation described in this paper proposes a solution to the problems of color dissimilarity for microscope slides digitalized by various equipment. A method of color standardization using color calibration microscope slide is proposed and a piece of software is provided. The proposed method is verified on microscope slides digitalized by various digitalization tools. It is shown that the resulting colors in two images of the same fragments are more similar to each other. The prepared microscope color calibration slide is adjusted to the standardization of immunohistochemically stained tissue samples but it can be easily adjusted to other types of staining. The software is independent from color, shape and location of color spots on the calibration slide so it can be easily used with the other calibration slides designed for different needs.

Keywords—digital pathology, color standardization, color correction, color normalization, whole slide image, microscopic image analysis;

I. INTRODUCTION Digital imaging in pathology has grown almost exponentially during the period of the last decade, owing to improvements in slide scanners and development of methodology of computer image processing [1]. Since the introduction of the first automated, high-resolution whole-slide imaging (WSI) system by Wetzel and Gilbertson in 1999 [2], the integrated systems with complex software tools are being developed [3]. This progress allows to move treatment into the field of personalized medicine [4] with emphasis on improving quality, patient safety, and diagnostic accuracy [5]. Undoubtedly, digital pathology networks (telepathology), based on WSI systems, play a crucial role in that growth, because they can solve the problem of consultation among regular hospitals and diagnostic centers [6]. The digital pathology has three important needs: the image resolution on the level that allows detail and texture observation, the color consistency during digitalization, and

the data-storage challenge as it produces gigabit files. The latter depends on the availability of inexpensive data storage devices on the market. Although the first two still depend on the progress in digitalization tool development, they can be supported by software solutions: the methods of sub pixel resolution and the method of the color standardization development. In this paper, the approach to the problem of color consistency and color similarity for images produced by different digitalization tools is proposed to be used as a part of MIAP [7] platform, developed for the use of pathologists. The problem of color consistency is caused by metamerism phenomena. It emerges when two colors appear identical under certain circumstances, but in others (e. g. examined by spectral distribution) - appear to be different [7, 8]. Metameric matches are quite common, especially in near neutral (grayish or whitish colors) or dark colors, all of which are frequently present in WSI images. Two types of metameric failures, called the illuminant and geometric, can occur in the process of digitalization by different digitalization tools. Two material samples can match under the same conditions, but mismatch with a change of conditions. Similarly, they can match when viewed from one angle, but mismatch under different viewing angle [8]. Different tools for microscope slide digitalization use various light sources. Mechanisms for changing the relative position of light source and detectors, in relation to the slide, also vary across the tools. This can lead to the metameric failure observation. But this problem is out of scope of the paper. The problem of color dissimilarity for WSI produced by different digitalization tools is called batch effect. It is a result of a variation in experimental protocols, image acquisition parameters, and tools. Batch effect is visible in any collaborative repository of histopathological and immunohistopathological images, where data is collected from many hospitals and clinics. So far, several methods of color management in histological slides digitalization tools were published. It was first proposed by Yagi [9] then by Murakami in 2012 [10]. These methods are based on pixels’ color statistics, which also vary and depend on the tissue and pathology presented in an image. A paper published by Revie and co-workers in 2014

978-1-5090-1817-8/16/$31.00 ©2016 IEEE

This full text paper was peer-reviewed at the direction of IEEE Instrumentation and Measurement Society prior to the acceptance and publication.

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[8] presents calibrating slides that are produced based on the biopolymers stained with typical dyes used in the pathology labs. But, they are still not available commercially. The method proposed by Bautista and co-workers in 2015 [11] converts colors in a digital slide according to information captured from color calibration slide, adjusted to slides stained with Hematoxylin and Eosin (H&E). This paper proposes a method for color standardization of immunohistochemically (IHC) stained samples, that has been developed to make WSI digitalized by various scanners and microscopes more similar in color. It is used in MIAP - web-based platform for the computer analysis of microscopic images to support the pathological diagnosis [7].

II. THE REASONS OF COLOR VARIATION IN DIGITALIZED IMAGES OF HISTOLOGICAL SLIDES

While the most common reason for the variation of color in image is the variance in the protocols and practices in the histology labs, the capture and display (monitor) parameters are also an important reason. It is difficult to identify which exactly causes the problem in particular case. The first reason of color performance variations, namely laboratory’s work standardization, is out of scope of this paper. To avoid its influence on the samples’ appearance, the tissue samples used in this investigation are prepared by only one laboratory in Pathology Department of Military Institute of Medicine (MIM) in Warsaw. The display color variations can be easily avoided by using typical Macbeth Color Chart and display analyzer with monitor calibrating tool [8]. We use self calibrating monitors EIZO ColorEdge CS230-BK + I1 Display PRO rent. This paper addresses a problem of color variation in images captured by various tools for microscope slides digitalization, such as scanners or microscopes. There are a lot of microscopes with cameras and several whole slide scanners [12] available on the market but vendors don’t share common standards for the color reproduction software and for components of scanners. They use various light sources, optics, sensors and mosaics in cameras. Furthermore, they use their own software for image reconstruction and for image presentation, so called slide viewers. Accordingly, each company adjusts their software to their own scanners. Since there is no standard for image format so far, the information about color can be coded and interpreted in various ways and formats [5]. It is difficult to determine whether the colors of a digital slide resemble colors of a microscope slide. So, there is a need to propose a procedure, which makes colors of two images created by various slides digitalization tools as similar as possible. This procedure, called color standardization, should be based on color phantoms. For scanners’ quality calibration or comparison the standard method assumes the use of the pre-prepared slides based on 15-day old or older mouse embryo paraffin block. Samples are sectioned by automatic machine with 3μm thickness and stained with H&E [8]. Since the variations in

color between stained slides are significant, color phantoms can be deemed useless. Moreover, the colors usually present in such samples are only pink to red (cytoplasm stained by E) and blue to purple (nuclei stained by H). In MIAP - web-based platform the majority of samples is stained indirectly with 3,3’-Diaminobenzidine (DAB). In these samples various shades of brown color, from dark orange or red to almost black [13 - 15] can be observed, as in Figures in this paper. To sum up, this paper presents the method of color standardization adjusted to the IHC stained tissue sample images.

Fig. 1. Color calibration glass microscope slide. PET film colors: (counterclockwise from red) red, gree, blue, yellow, amber, white, gray, brown. Brown, amber and yellow are generally observed in DAB&H tissue slides, while the rest of colors are primary set. All color spots are shaped as triangles and arranged in such a way to be close to each other in the center. The top row consist of ideal color representations with values in CIELAB color space for the used films.

III. MATERIAL AND METHODS The proposed method consists of a microscope

calibration slide construction method and two pieces of software: for the creation of a calibration matrix and for WSI color correction. The method has been validated using pairs of IHC stained images digitalized by two different digitalization tools described farther.

A. Calibration slide and software for color standardization The construction of a calibration slide takes into account that color differences among images of IHC stained samples are larger than among images of H&E and that pale color fragments are more frequent in the former. The proposed microscope slide is constructed from the following color phantoms: red, green, blue, and 50% gray as primary colors, and also additional colors, which are generally observed in DAB&H tissue slides: brown, amber, yellow (see Fig. 1). This color calibration microscope slide is made using a typical glass slide on which color samples of polycarbonate plastic and deep dyed polyester film [16] with known light transmittance (Substrate: PET – Polyethylene Terephathalate) from ROSCO LTD are fixed. The fixation is done using cover glass embedded with double-sided adhesive tape. All color

49

spots are shaped as triangles and arranged in such a way to be close to each other in the center, as in Fig. 1. These color spots interact with light similarly to stained tissue sections. They absorb light of certain spectral wavelengths and transmit the other spectral wavelengths. The color correction microscope slide is digitalized using particular digitalization tool and this image is used to prepare matrix of coefficients for color correction. The software consists of two parts which perform two consecutive operations: the first one produces the correction matrix which is used in the second step to correct colors in WSI digitalized by the tool for which correction matrix had been calculated. The part of the software for the development of correction matrix segments an image of digitalized microscopic slide using a neural network. It divides image into 9 clusters (all of the colors and additionally very dark pixels called ‘black’). This software works independently from shape and location of each of 8 color samples. Median of color in each cluster (so called real values) and each color sample characteristics known from film producer (so called perfect values) are used to calculate color correction matrix according to equation:

×

=

BBGGRR

BBGGRR

MMMMMMMMM

pp

pp

pp

bb

bb

bb

BBB

GGG

RRR

pinv

81

81

81

81

81

81

321

321

321

...

...

...

...

...

... (1)

where the correction matrix (M) is a result of multiplication of base (perfect) RGB coordinates (3x8 matrix) by the pseudoinverse (8x3 matrix) of captured (real) RGB coordinates from the slide. The rows in the correction matrix are indexed by their impact on the R, G or B coordinates in the corrected image. All these calculations are performed in linear RGB which means that the RGB values are transformed to inverse gamma correction by the acquisition tool [17]. Each digitalization tool can be associated with its correction matrix, which is calculated and ready to use on the MIAP - web-based platform. So, each new WSI or image digitalized using this tool in the same circumstances can be corrected on the MIAP.

B. Experimental material Two microscope slides with tissue sections stained with DAB&H from two patients (first with meningioma and the second with oligodendroglioma) were prepared in Pathology Department of MIM according to the protocol described in [18 - 21]. These slides have been scanned using three digitalization tools. The first tool is a microscope: Olympus IX70 with Plan APO 40x objective and with camera Qicam (Qimiging company) that collects three monochromatic images with RGB filters merged together into color image. So, all pixel intensities are measured (not interpolated using Bayer filter) which leads to an assumption that color acquisition is more realistic. The process of acquisition is controlled by software ImagePro Plus and ImagePro Premier (Media Cybernetics).

The second tool is a motorized microscope: CoolScope S1 (Nikon) which is an early version of WSI scanner. The third tool is Pannoramic DESK from 3DHISTECH Ltd which is a new solution on the market with good efficiency and quality of scanning process. All acquisition tools can work automatically in tile mode, so they automatically merge fields of view to create final image.

C. Methods of validation To compare the distances of pairs of images quantitatively, two types of evaluation techniques are used: the distances in CIELAB and mean squared error in RGB between colors of corresponding pixel in both images [22-26]. According to [11] it was assumed that images are considered to be perceptually similar in color if the difference in CIELAB is below 2.2 units [23].

Fig. 2. Two images matching process by SIFT. To compare images from different scanning tools matching and transforming procedure should be performed first. All characteristic points are identified (left). Then, matching points on which transform function is defined are selected (right). The fragment is scaled to fit pixel by pixel to the same fragment.

These types of quantitative comparison required image adjustment: similar pixel size and corresponding objects co-localization. Using simplified artificial images and experimental images collected by various digitalization tools showing the same microscope slide area after adjustment, the comparison was performed. Since images from each scanning tool have different size in pixels and different optical distortion embedded in images, any comparison should be performed after matching and transforming procedure. The MATLAB software for matching and calculating transformation and matching one image to the other with bicubic interpolation has been used to adjust images from different scanners. Bicubic interpolation method was chosen, according to the results of the IHC stained tissue digital images interpolation methods comparison, published in [26 - 28]. The matching procedure is based on identifying all characteristic points (left part of Fig. 2) in both images, selecting some of them to define transformation (right part of Fig. 2) then filling the smaller image with zeros to the size of bigger one. The small fragment is scaled to fit pixel by pixel to

50

the same fragment from the bigger image. Fig. 2 presents the result of finding matching points by SIFT. To show the difference in colors, two graphs of colors presented in corresponding fragments of images have been constructed (presented in Fig. 3). These graphs show chromaticity space - a versus b before and after correction. This comparison is useful as it does not require image adjustment.

IV. RESULTS AND DISCUSSUION The left column of Fig. 3. presents two image fragments digitalized by microscope Olympus (top) and CoolScope (middle). Microscope slide fragment before color standardization (left) and after (right) are shown in the first two rows. Graphs in the bottom row demonstrate a-b chromaticity space with colors present in image digitalized by microscope in yellow and digitalized by CoolScope in blue. The left diagram presents images before while right - images after color correction. It is clearly visible that the size of color subspace which overlaps in both images (green part) increases after color standardization. The color variation in uncorrected pair of images is evident (left column), while such color variations in color-corrected pair of images (right column) among blue and brown objects is decreased but increased in the background.

Fig. 3. Two images of fragments of microscope slide before (left column) and after (right column) color standardization. The chromatin space a-b with colors presented in images digitalized by two digitalization tools before (bottom left) and after (bottom right) color standardization. The colors in the image digitalized by microscope are shown in yellow and digitalized by CoolScope in blue. The size of color subspace which overlaps in both images (in green) increases after color standardization.

Table I presents results of quantitative color comparison for artificial and experimental images. The standardized distance

in CIELAB and mean squared error in RGB are calculated between two images. In the case of artificial images these quantitative measurements are calculated for the reference image (filled with colors transmitted by each polyester film) and images developed using median colors of each color scanned version for each of scanning tool separtely. These artificial images are developed in two versions – without and with gaussian noise (mean = 0 and variance = 0.002) added. Last row of the Table I presents mean and standard deviation of distance and mean square error calculated for 15 fragments of tissue samples. The standarized CIELAB distance and mean squared error calculated in RGB for artifical images after color correction appears to be smaller and with lower error, in comparison to images without color correction. The differences presented in Table I do not exceed the level of the perceptual similarity. The mean distances between pairs of images presenting the same fragment from both tissue samples (before correction) are 6.4±0.76 and 12.4±1.3 respectively in CIELAB and in RGB. If images are corrected using corresponding matrices, the standarized distances between corrected images are 9.3±0.7 and 8.7±1.7 respectively. It seems that the mean squared measurement in RGB illustrates that colors after correction are closer than before correction. This is not visible in the standardized distance in CIELAB. It can be explained by a presence of noise which influences pixels’ color after correction which is also visible while comparing artificial images with and without noise added.

TABLE I. QUANTITATIVE COMAPRISION OF THE PAIRS OF IMAGES In the case of artificial images these quantitative measurements are calculated for the reference image (filled with colors transmitted by each polyester film) and images developed using median colors of each color scanned version for

each of scanning tool separtely. In the last row are mean and standard deviation of the results calculated for 15 fragments of tissue samples.

Image pairs of micros-

cope slides

Comparison

Scanner

Normalized distance in CIELAB

(mean / standard deviation)

Mean square error calculated in RGB (mean / standard

deviation)

Before After Before After Reference

vs Artificial – without

noise

CoolScope S1 0.0016* 0.0004* 11.2 9.1

Pannoramic DESK 0.0036* 0.0014* 11.3 9.7

Reference vs

Artificial – with noise

CoolScope S1 0.0015* 0.0008* 10.9 10.8

Pannoramic DESK 0.0036* 0.0022* 11.3 10.6

Fragments of

experimental

samples

CoolScope S1

6,4±0.76 9,4±0.7 12.4±1.3& 8.7±1.7& Pannoramic

DESK

*perceptually similar &statistically significant p

V. CONCLUSIONS This paper proposes the method for color standardization in histological digital images or WSI. The method deals with color variations, which are a result of different optics, cameras and software in various digitalization tools. This method is applied in the MIAP platform [7] developed in MIM by consortium of three institutions: MIM, Nalecz IBBE, and WUT under national applied project. The MIAP offers tools for quantitative analysis of digital images of IHC stained tissue samples, some of which are based on color and texture features. This analysis results may become a useful tool for diagnostics and prognostics system in the future. The method of color standardization, which is presented in the paper, makes WSI and images’ colors more similar to each other. It increases applicability of quantitative analysis method to images and WSI, as well as prevents diagnostic and prognostics errors. The method is simple as the data needed for color standardization is extracted from the digital version of the color calibration slide and from known characteristic of color spots used in correction microscope slide. Limitations of this solution include: lack of fully automatic color finding algorithm for the calibration procedure, (2nd) sensitivity to changes in acquisition settings and (3rd) a necessity for several color calibrations for the same scanner/microscope, and (4th) a necessity to provide calibration slide to the digitalization equipment. The first problem is an issue of software development. The solution is now being pursued using neural network method, which already works properly for used set of images. The 2nd, 3rd and 4th are intertwined, and will require further research and development of an acquisition procedure. The proposed 8 colors of samples are selected to correct DAB&H stained images. With an addition of dynamic neural network learning, the software for correction matrix calculation and digital standardization could be independent from colors of samples and their position on the glass. So, it could be easily adapted to other types of slides and other spots’ location and shape. In future work, we plan to create different calibration slides for other types of staining than currently analyzed DAB&H. Furthermore, the strong emphasis will be put on developing a fully automatic software for finding all eight colors in the scanned slide. Secondly, the changes of correction matrices should be investigated. The main subject of the investigation should be the changes of the values in the matrices, depending on typical errors/changes in the image: noise, blurring, brightness and vignetting.

Acknowledgment This study was supported by the National Centre for

Research and Development, Poland (grant PBS2/A9/21/2013). We all thank Anna Wesołowska-Szcześniak, Marek Dawgul and Konrad Dudzinski for help calibration glass assembling and cleansing.

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/ColorImageDict > /JPEG2000ColorACSImageDict > /JPEG2000ColorImageDict > /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 150 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages true /GrayImageDownsampleType /Bicubic /GrayImageResolution 300 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 2.00333 /EncodeGrayImages true /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict > /GrayImageDict > /JPEG2000GrayACSImageDict > /JPEG2000GrayImageDict > /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 1200 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages true /MonoImageDownsampleType /Bicubic /MonoImageResolution 600 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.00167 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict > /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (None) /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False

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color standardization for the …black [13 - 15] can be observed, as in figures in this paper. to...

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