Segmentation and morphological analysis of microvessels in immunostained histological tumour sectionsA fully automatic segmentation and morphological analysis algorithm for the analy- sis of microvessels from CD31 immunostained histological tumour sections is presented. The algorithm exploited the distinctive hues of stained vascular endothelial cells, cell nuclei and background, which provided the seeds for a region-growing algorithm in the 3D Hue, Saturation, Value (HSV) colour model. The segmented objects, identified as microvessels by CD31 immunostaining, were post-processed with three morphological tasks: joining separate objects that were likely to belong to a single vessel, closing ob- jects that had a narrow gap around their periphery, and splitting objects with multiple lumina into individual vessels

Akerman, S.

Kanthou, C.

Reyes-Aldasoro, C. C.

Tozer, G. M.

Williams, L. J.

City Research Online

Reyes-Aldasoro, C. C., Williams, L. J., Akerman, S., Kanthou, C. & Tozer, G. M. (2010). Segmentation and morphological analysis of microvessels in immunostained histological tumour sections. Paper presented at the Medical Image Understanding and Analysis, 06-07-2010 - 07-07-2010, Warwick, Uk. City Research OnlineOriginal citation: Reyes-Aldasoro, C. C., Williams, L. J., Akerman, S., Kanthou, C. & Tozer, G. M. (2010). Segmentation and morphological analysis of microvessels in immunostained histological tumour sections. Paper presented at the Medical Image Understanding and Analysis, 06-07-2010 - 07-07-2010, Warwick, Uk. Permanent City Research Online URL: http://openaccess.city.ac.uk/4644/ Copyright & reuseCity University London has developed City Research Online so that its users may access the research outputs of City University London's staff. Copyright © and Moral Rights for this paper are retained by the individual author(s) and/ or other copyright holders.  All material in City Research Online is checked for eligibility for copyright before being made available in the live archive. URLs from City Research Online may be freely distributed and linked to from other web pages. Versions of researchThe version in City Research Online may differ from the final published version. Users are advised to check the Permanent City Research Online URL above for the status of the paper.EnquiriesIf you have any enquiries about any aspect of City Research Online, or if you wish to make contact with the author(s) of this paper, please email the team at publications@city.ac.uk.IMMUNOSTAINED MICROVESSEL SEGMENTATION: MIUA 2010 1Segmentation and morphological analysis ofmicrovessels in immunostained histologicaltumour sectionsConstantino Carlos Reyes-Aldasoro,Leigh J. Williams, Simon Akerman,Chryso Kanthou, Gillian M. Tozerhttp://tmg.group.shef.ac.ukCancer Research UK Tumour Micro-circulation Group, Department of On-cology, The University of Sheffield,Sheffield, S10 2RX, UK.AbstractA fully automatic segmentation and morphological analysis algorithm for the analy-sis of microvessels from CD31 immunostained histological tumour sections is presented.The algorithm exploited the distinctive hues of stained vascular endothelial cells, cellnuclei and background, which provided the seeds for a region-growing algorithm in the3D Hue, Saturation, Value (HSV ) colour model. The segmented objects, identified asmicrovessels by CD31 immunostaining, were post-processed with three morphologicaltasks: joining separate objects that were likely to belong to a single vessel, closing ob-jects that had a narrow gap around their periphery, and splitting objects with multiplelumina into individual vessels. Keywords: vessel segmentation and morphology, IHC.1 IntroductionThe immunohistochemistry (IHC) for staining of tissue sections for different proteins is astandard method for diagnostic and research purposes. Staining for platelet endothelial cell(EC) adhesion molecule (PECAM-1 / CD31) with tagged antibodies is an effective methodfor identifying and localising the ECs that line blood vessels, as CD31 is expressed consti-tutively on the surface of adult, embryonic and tumour ECs. In oncology, the expressionof CD31 by endothelial cells in angiogenic vessels has gained considerable attention as thetumour vasculature is emerging as an important therapeutic target for cancer. Despite thepopularity of the use of IHC to stain for different proteins and the growth and power of com-puter and image analysis algorithms, manual procedures are still the most common methodfor assessing the presence, absence, distribution or intensity of staining[1, 2].Numerous algorithms for IHC image analysis have been developed for different specifictasks: counting objects such as nuclei, cells or microvessels, quantifying optical density,measuring the abundance of a stain, or extracting morphometric measurements such as area,perimeter, ratio or perimeter and area and angle of microvessels [3, 4, 5]. Many of thesealgorithms are semi-automated, but most require a certain degree of user interaction, eitherfor (a) pre-processing tasks such as adjustments of brightness and contrast or selection ofwindow sizes, counting of vessels or demarcation of regions of interest or vessels, (b) post-processing tasks such as delineation of lumina for segmented objects, inclusion/exclusion ofregions of interest, elimination of artefacts or (c) training of classifiers with supervised data.c© 2010. The copyright of this document resides with its authors.It may be distributed unchanged freely in print or electronic forms.2 IMMUNOSTAINED MICROVESSEL SEGMENTATION: MIUA 2010Most of these algorithms use either the Red, Green and Blue (RGB) channels, a subsetof these or the grey level intesity equivalent from the colour images, and few exploit thehigher discrimination that can be achieved when the RGB channels are transformed to theHue, Saturation and Value (HSV ) channels. Post-processing of the segmented objects, aspresented in this paper, is desirable to introduce a higher reliability of any morphometricmeasurements and therefore all the statistical measurements derived from these.2 Segmentation AlgorithmThe segmentation algorithm was based on a transformation from the RGB to the HSV colourmodel. HSV describes perceptual colour relationships related to the artistic ideas of hue, tintand shade [7] enabling a 3D chromatic histogram to be constructed from the HSV images.In this work, we considered that a colour image Irgb had dimensions Nr ×Nc × 3 forrows, columns and three colour channels that were quantised to Ni levels, which are usually256. Let Lr = {1,2, ...,r, ...,Nr}, Lc = {1,2, ...,c, ...,Nc} be the spatial domains of the data,x ∈ (Lr×Lc) be a pixel of the image, and [R,G,B] =[{1,2, ...,r, ...,Ni} ,{1,2, ...,g, ...,Ni},{1,2, ...,b, ...,Ni}] a triplet of RGB values. An image was represented then as a function thatassigned a colour to each pair of co-ordinates: Lr×Lc; Irgb : Lr×Lc → [R,G,B].The shading of an original unbiased imageU , which was corrupted by a slowly-varyingshading S so that I =U +S, was corrected by estimating S as the envelope of the signal [6]and removing it from the biased image I˜rgb ≈U = I−S. Then, the mean value of the threeRGB unbiased channels was equalised to obtain a background with minimum saturation.The unshaded and equalised image RGB was converted to an HSV colour model: Ihsv =T(I˜rgb), Ihsv = [Ihue, Isat , Ival ]. The hue-saturation-value histogram mHSV (h,s,v) is a tri-variate measurement of the occurrence of [H,S,V ] on Ihsv and it was defined as:mHSV (h,s,v) =#{x ∈ (Lr×Lc) : Ihue(x) = h, Isat(x) = s, Ival(x) = v}#{Lr×Lc} ,where # denoted the number of elements and h ∈ H, s ∈ S, v ∈ V . The 2D hue-valuemHV (h,v), hue-saturation mHS (h,s) or saturation-value mSV (s,v) histograms are marginaldistributions of mHSV (h,s,v). For this work, we introduced the maximum saturation pro-file as measurement of the distribution of the highest value of saturation for every value ofhue, mathematically: pmaxS ={y ∈ (h× S) : max(s) so that mHS(y) > 0}, h ∈ H. Whilethe marginal distributions revealed the frequency of pixels within a certain hue or saturation,pmaxS revealed the distribution of saturated regions. Fig. 1 shows a representative IHC imageand its final segmentation, mHSV as a cloud of coloured points and an RGB cloud to comparethe separability of the HSV model, mHS as a mesh overlaid on the 2D loci of constant valueand pmaxS together with the histogram mH .Three criteria defined the brown colour of the endothelial cells, low value (v < I¯val),high saturation (s > 0.25) and an adaptive hue range. The colour variations inherent toIHC [8] resulted in images where the colour of stained nuclei was either blue or light purpleand the ECs were brown to light beige. To compensate for this variation, the amount ofbrown (0◦,67◦) was measured and used to determine the range of hues that were selected asbrown. For those images with a strong component of brown, ranges of brown, backgroundand blue were approximately (10◦,50◦), (60◦,200◦), (220◦,310◦) while for those with a lowcomponent it was (350◦,90◦), (90◦,190◦), (200◦,290◦) respectively.The segmented ECs were used as seeds in a region growing algorithm, with the followingcriteria of similarity: (a) a combination of the s and v, distances in (b) position and (c)IMMUNOSTAINED MICROVESSEL SEGMENTATION: MIUA 2010 3hues from the pixels to the seeds. Stop criterion was minimal change (10 pixels) betweeniterations. Small (1 or 2 pixels) and isolated regions were discarded. Unassigned pixels wereconsidered as background.Three morphological conditions were used to assign objects as vessels: (a) join objectswhich could form part of a single vessel (Fig. 2a). Solid objects close to each other wereconsidered to be joined. A skeleton of the individual objects was compared with one of thejoined ones and only when the combined had lower number of branching points the objectswere joined. (b) Close the open objects (Fig. 2b), Small gaps were closed by using thecombination of the external edge of the object with the watersheds of a distance map ofthe background. (c) Split joined objects, e.g. a single vessel that turned in the up-downdimension (Fig. 2c). The larger holes of the lumina were used to generate a distance mapthat was split with the watershed transform to partition the original object into smaller ones.3 Morphological AnalysisThe segmentation algorithm described above provided a series of objects that described theendothelial cells of a tumour that were stained for CD31 and as such the objects describedclosely the shape of the microvessels of the tumour, as they appear in 2-dimensions. The laststage of the segmentation algorithm was to obtain a series of measurements that providedmorphological information about the vasculature of the tumour. The following measure-ments were extracted from each individual vessel (segmented objects):(1) SA - Area of segmented object excluding lumen (Stained Area [µm2]) (2) VA - Areaof segmented object including lumen (Vascular Area [µm2]), (3) lu/VA - Ratio of lumen tovascular area, (4) e - Eccentricity of the vessel: e=√1− ma2MA2where (MA, ma) are the majorand minor axes of an equivalent ellipse, (5) ro -Roundness of the external boundary of thevessel: ro = P/√4piVA where P is the actual perimeter of the object and VA is the vesselarea, (6) rVA - relative Vascular Area as the ratio of the total Vascular Area (sum of the areasof all objects in the image) relative to the total area of the image. This metric indicates theextent of vascularisation of the tumour.4 Results and DiscussionThe robustness of the segmentation algorithm lies with the chromatic characteristics of theimmunostained vessels of the tumours and the intrinsic difference with the haematoxylin-stained nuclei of tumour cells. Although there are differences in the shades obtained fromthe IHC process, the endothelial cells can be better discriminated in the HSV channels thanin the traditional RGB channels.Fig. 4 shows the histograms of the following morphometric measurements: (a) VA, (b)lu/VA, (c) e, (d) ro, and (e) rVA. Forty-four images were acquired from the three differenttumours and 6,163 vessels were segmented in total. The data for each tumour are shownseparately, as indicated in the legend. Some differences in the vascular morphology of thethree tumours are apparent in the histograms. For instance, a higher proportion of larger-sized vessels (as measured by VA) are shown in tumour 2 compared with tumours 1 and 3(Fig. 4a). The larger vessels in tumour 2 were also associated with larger lumina relativeto VA (Fig 4b) and combined with the density of the vessels, the rVA i.e. the extent ofvascularisation was also larger in tumour 2 than in tumours 1 and 3 (Fig 4e). In the future4 IMMUNOSTAINED MICROVESSEL SEGMENTATION: MIUA 2010we expect to use this algorithm to compare the measurements in different populations, forinstance, tumours treated with a certain drug against a control or regional variation accordingto the position within the tumour.The strength of the algorithm resides with the large number of microvessels that can beanalysed by a fully automatic segmentation. The automatic segmentation also ensures con-sistent criteria for object identification and allows the process to be run on a set of images inthe background. In addition, the algorithm provides morphometric analysis of microvesselsfrom which general population statistics can be calculated and has general applicability fora range of different tissues and therapeutic interventions.This work was funded by Cancer Research UK.(a)(d)(c)(b)(e) (f)Figure 1: (a) Immunostained tumour section, nuclei appear blue-purple, ECs brown-beigeand background in white-grey (bar = 80 µm). (b) A cloud of coloured points describes thechromatic distribution of (a) in the RGB domain. (c) A cloud describing mHSV within theHSV domain. Notice the discrimination between hues in (c) that is not visible in (b).(d) Finalsegmentation of (a). (e) mHS overlaid on the loci of constant value. (f) pmaxS and mH .(a)(c)(b)Figure 2: Morphological analysis: (a) joining 2 separate objects, skeleton (yellow) andbranching points (brown) of individual objects (blue). (b) Closing of open objects, watershedtransformation (blue lines), external boundaries of the original object (white) and the newobject (blue) (c) Splitting of objects that may correspond to more than one vessel.IMMUNOSTAINED MICROVESSEL SEGMENTATION: MIUA 2010 5(a) (c)(b)Figure 3: (a) IHC image with the boundaries of the segmented vessels overlaid with a greenline. (b) Segmented objects. The region of interest denoted by the white box is shown indetail in (c).0 1000 2000 30000.050.10.150.20.250.30.350.4Vascular Area [  µm2 ]   VA Relative Frequency  0 0.2 0.40.10.20.30.40.50.60.7Lumen/Vascular Area   lu/VA 0.6 0.8 10.050.10.150.20.250.30.35Eccentricity  e 1 2 3 40.050.10.150.20.250.3Roundness  ro 0.05 0.1 0.1500.10.20.30.40.50.60.70.8Relative Area  rVA Tumour 1Tumour 2Tumour 3Figure 4: Histograms for the morphometric measurements of microvessels from 3 tumours.(a) VA, (b) lu/VA, (c) e, (d) ro, and (e) rVA. Solid blue, dashed red line and dash-dot blacklines correspond to tumours 1-3 respectively. Parameters in (a-d) are acquired per objectfrom 12-17 images per tumour, whereas a single value per image is acquired for rVA in (e).References[1] M. A. den Bakker, S. J. Flood, and M. Kliffen. CD31 staining in epithelioid sarcoma. Virchows Arch, 443(1):93–7, 2003.[2] S. Enomoto, M. Sata, D. Fukuda, K. Nakamura, and R. Nagai. Rosuvastatin prevents endothelial celldeath and reduces atherosclerotic lesion formation in apoe-deficient mice. Biomed Pharmacother, 63:19–26, 2009.[3] J. C. Goddard, C. D. Sutton, P. D. Furness, R. C. Kockelbergh, and K. J. O’Byrne. A computer imageanalysis system for microvessel density measurement in solid tumours. Angiogenesis, 5:15–20, 2002.[4] D. Kim, C. W. Gregory, G. J. Smith, and J. L. Mohler. Immunohistochemical quantitation of androgenreceptor expression using color video image analysis. Cytometry, 35(1):2–10, 1999.[5] P. Ranefall, L. Egevad, B. Nordin, and E. Bengtsson. A new method for segmentation of colour imagesapplied to immunohistochemically stained cell nuclei. Analytical Cellular Pathology, 15(3):145–156, 1997.[6] C. C. Reyes-Aldasoro. Retrospective shading correction algorithm based on signal envelope estimation.Electronics Letters, 45(9):454–455, 2009.[7] A. R. Smith. Color gamut transform pairs. ACM Siggraph Computer Graphics, 12(3):12–19, 1978.[8] C. R. Taylor and R. M. Levenson. Quantification of immunohistochemistry - issues concerning methods,utility and semiquantitative assessment ii. Histopathology, 49(4):411–424, 2006.

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