an entirely automated method to score dss-induced colitis ...devise an entirely automated method to...

INTRODUCTIONInflammatory bowel disease (IBD) is a disorder of the digestive tractand affects more than 1 million people in the United States alone(Abraham and Cho, 2009). In ulcerative colitis (UC), a type of IBD,inflammation occurs primarily in the mucosa of the large intestines,leading to debilitating conditions including diarrhea, rectal bleedingand weight loss.

Although both genetic and non-genetic factors are associatedwith the disease, it is thought that UC is largely caused by aninappropriate inflammatory response by the host to intestinalmicrobes penetrating through a damaged epithelial barrier (Xavierand Podolsky, 2007). The enormous variety of gut flora contributesto the heterogeneity of the disease. This complexity might explainwhy currently available therapies for UC have only a 50% chanceof positive outcome for patients (Pastorelli et al., 2009).

In recent years, an ever growing number of drugs have beentested to treat UC, based on various strategies to regulate theimmune response, including steroids, immunomodulators, andantibodies against inflammatory cytokines, with variable success(Pastorelli et al., 2009). To speed up drug discovery, new candidatedrugs need to be efficiently screened in appropriate model systemsthat have clinical relevance.

Mouse (Mus musculus) models are invaluable for this purposebecause they are one of the least expensive systems in whichmammalian host-microbe relationships can be studied. In thedextran sulfate sodium (DSS) model of colitis, DSS polymers aredissolved in the drinking water of wild-type mice for 5 days (Wirtzet al., 2007). The resulting chemical damage to the mucus layerprotecting the epithelial tissue of the colon allows microbe entryinto the gut, causing acute inflammation (Johansson et al., 2010).Macroscopically, weight loss, diarrhea and shortening of the colonare observed (Diaz-Granados et al., 2000; Yan et al., 2009).Microscopic observation shows a progressive loss of intestinal cryptcells predominantly near the rectum and in the distal regions ofthe colon. Concomitantly, there is an increase in innate immunecells (neutrophils, macrophages, dendritic cells and natural killerT cells) and lymphocytes (B cells and T cells) in the intestinalmucosa (Yan et al., 2009; Hall et al., 2011). The inflammatoryresponse is thought to cause further erosion and ulceration ofcrypts. These microscopic observations, as well as macroscopicsigns, recapitulate aspects of the human disease.

Putative therapeutics can be found by searching for molecularagents that effectively reduce pathological scores in colitic mice.These scores are calculated by experienced pathologists based onmicroscopic observations, through examining hematoxylin andeosin (H&E)-stained slides of the gut (Wirtz et al., 2007). Becausemanual scoring is time-consuming and requires a specializedpathologist, it easily becomes a bottleneck in the drug-discoveryprocess.

In order to reduce the reliance on manual assessment ofmicroscopic findings, we set out to develop a fully automatedmethod to score DSS-induced colitis in mice by using image analysissoftware, Definiens. The ‘Developer’ module of Definiens allows thedevelopment of algorithms that interpret H&E slide images using an‘object-oriented’ method (Baatz et al., 2009). A ruleset (algorithm)

Disease Models & Mechanisms 855

Disease Models & Mechanisms 6, 855-865 (2013) doi:10.1242/dmm.011759

1Department of Pathology, Safety Assessment, 2Department of TranslationalImmunology, Research, 3Department of Bioinformatics, Research, 4Department ofImmunology, Research, and 5Department of Pathology, Research, Genentech Inc.,South San Francisco, CA 94080, USA*Author for correspondence ([email protected])

Received 10 January 2013; Accepted 19 February 2013

© 2013. Published by The Company of Biologists LtdThis is an Open Access article distributed under the terms of the Creative Commons AttributionNon-Commercial Share Alike License (http://creativecommons.org/licenses/by-nc-sa/3.0), whichpermits unrestricted non-commercial use, distribution and reproduction in any medium providedthat the original work is properly cited and all further distributions of the work or adaptation aresubject to the same Creative Commons License terms.

SUMMARY

The DSS (dextran sulfate sodium) model of colitis is a mouse model of inflammatory bowel disease. Microscopic symptoms include loss of cryptcells from the gut lining and infiltration of inflammatory cells into the colon. An experienced pathologist requires several hours per study to scorehistological changes in selected regions of the mouse gut. In order to increase the efficiency of scoring, Definiens Developer software was used todevise an entirely automated method to quantify histological changes in the whole H&E slide. When the algorithm was applied to slides fromhistorical drug-discovery studies, automated scores classified 88% of drug candidates in the same way as pathologists’ scores. In addition, anotherautomated image analysis method was developed to quantify colon-infiltrating macrophages, neutrophils, B cells and T cells in immunohistochemicalstains of serial sections of the H&E slides. The timing of neutrophil and macrophage infiltration had the highest correlation to pathological changes,whereas T and B cell infiltration occurred later. Thus, automated image analysis enables quantitative comparisons between tissue morphology changesand cell-infiltration dynamics.

An entirely automated method to score DSS-inducedcolitis in mice by digital image analysis of pathologyslidesCleopatra Kozlowski1,*, Surinder Jeet2, Joseph Beyer1, Steve Guerrero3, Justin Lesch2, Xiaoting Wang4, Jason DeVoss2 and Lauri Diehl5

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was designed using Developer, which automatically evaluates theextent of crypt loss in an H&E colon slide.

Another method commonly used to study colitis isimmunohistochemistry (IHC). Because immunomodulation is aprimary target for potential therapeutics, it is informative toexamine colon slides labeled with immune cell markers.Quantification of stain distribution is challenging to the humaneye, but can be performed efficiently using Definiens (Diaz et al.,2012).

The methods presented here can decrease the labor required bypathologists and speed up the identification of potential therapeutictargets for patients suffering from UC.

RESULTSEvaluation of historical studiesIn order to decrease the time required for microscopic scoring ofcolon pathology in the DSS-induced model of colitis, an automatedalgorithm was developed using specialized image analysis software,Definiens, and applied to whole scanned and digitized H&E slidesof mouse large intestines. For an automated algorithm to be useful,it must be significantly faster and less labor intensive than apathologist’s manual scoring system, and allow the identificationof candidate therapeutics in drug-discovery studies in a way thatis comparable to using manual scores. To test this, H&E colon slidesfrom nine historical studies with a total of 544 colon samples, forwhich manual scores had already been obtained, were analyzed.

Speed of scoringAll studies had been scored manually by one of two experiencedpathologists (Table 1). The manual scoring process required up to5 minutes per slide. The automated algorithm required an averagecomputing time of just under 15 minutes per slide per processor but,using 28 central processing units (CPUs), the entire sample set (544animals) was processed in ~4.5 hours, effectively taking 30 secondsper slide. No manual intervention was necessary, except loading theslides (~4 seconds per slide, or ~36 minutes for 544 samples) andinitiating the computation, which took a minute or less per study.

Score normalizationThe studies were designed to address specific hypotheses regardingthe effects of molecular agents on colitis scores in miceadministered DSS. All studies contained three types of controlgroups: animals given water without DSS (Ø control), mice givenDSS alone or DSS combined with an inert antibody (–ve control)and mice administered a molecule known to reduce colitis severity,IL-22 Fc (Sugimoto et al., 2008), at various doses (+ve control). Inaddition, each study contained groups of animals given DSS andseveral molecular agents, or a single agent at varying doses.Because there was large variation of raw scores given to equivalentcontrol groups in different studies (Fig. 1), raw scores werenormalized relative to the mean scores of the Ø and –ve controlgroups in each study.

Role of effect size and P-values in drug target identificationFor correct identification of efficacious drug candidates, theautomated method needs to be able to identify treatment groupsthat had scores that were statistically different from those in the–ve control group. However, it might not be able to do so at alleffect sizes. Consider, for example, the dose titration study #7(Fig. 2): in this particular study, IL-22 Fc, which in other studieswas used as a +ve control at only one dose, was instead given atdoses titrated from 30 to 0.05 μg in combination with DSS. Bothautomated and manual normalized scores identified a statisticallysignificant difference from the –ve control at the 30 μg and 6 μgdose (Table 2). At 1.25 μg, both automated and manual scores failedto reach statistical significance but, at 0.25 μg, the manual scoreidentified a statistically significant difference not found by theautomated method. However, the effect size of the 0.25 μg doselevel is small: less than 20%. Because statistical difference was notobserved at the 1.25 μg level by either manual or automated scores,

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Automated pathology scoring in DSS colitisRESOURCE ARTICLE

RESOURCE IMPACT

BackgroundColitis is a highly debilitating disease that affects over a million people in theUnited States alone. Half of these patients do not respond well to availabletherapies, highlighting a serious, unmet medical need. Mouse models of colitisare routinely used to screen for potential therapeutics, but the screening timerequired for a pathologist to score their colon slides manually is a majorbottleneck in the drug-discovery process. There is a possibility of automatingthe process by digital image analysis; however, this requires analysis ofhematoxylin and eosin (H&E) slides, which are generally far more difficult toanalyze than immunohistochemistry (IHC) slides owing to their signalcomplexity and variability. Consequently, few attempts have been made toanalyze H&E slides thus far, even though it is one of the most common stainsused by pathologists for a wide variety of applications.

ResultsThe authors propose a new automated image analysis method to quantifyresponses to treatment, using the dextran sulfate sodium (DSS) mouse modelof inflammatory bowel disease. This method quantifies responses on the basisof histological changes in the mouse tissue, which are scored on whole H&Eslides. The authors demonstrate that the method is sufficiently predictive androbust to support its use in a drug-screening setting. Furthermore, the image-analysis method can be combined with more traditional IHC quantificationtechniques to gain further insight into the molecular mechanism of action of aparticular therapeutic candidate.

Implications and future directionsIn addition to its potential for application in research involving the DSS mousemodel of colitis, this work is likely to encourage more research into applyingimage analysis techniques to quantify tissue morphology changes on H&Eslides. Considering the similarities in tissue structure between mice andhumans, it is conceivable that the method presented here will, in the future,be used to analyze colon tissue from human biopsies, which could help toaccelerate clinical diagnoses of colitis.

Table 1. Study numbers and dose of IL-22 Fc used as a +ve control

Study number IL-22 Fc dose (μg)/mouse (+ve control)

1 100

2 100

3 100

4 30

5 100

6 30

7 Dose titration 30-0.05

8* 30

9* 200 *Studies scored by a different pathologist from the other studies.

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this might or might not be a ‘true’ result. In our experience ingeneral, it is unlikely that an effect size of <20% is a biologicallymeaningful result. The drug candidate identification process thusdepends on both effect size (difference between the means of the–ve control group and test group) and statistically significantdifferences based also on variance (the result of a Student’s t-testbetween the –ve control group and test group).

Identification of treatment candidates using the automatedalgorithmApplying the reasoning above, we determined how many test groupsthat were identified as being effective at reducing colitic scores using

normalized manual scores would be found using normalizedautomated scores. ‘Hits’ were defined as those test groups for whichmean scores obtained at least a 20% effect size and a Student’s t-testP-value <0.05 compared with –ve controls. Out of 50 test groups,13 were found to be a hit by both automated and manual methods(true positives), 0 were found to be a hit only by the automatedmethod (false positive), 6 were found to be a hit only by the manualmethod (false negative), and 31 were found to not be hits by eithermethod (true negatives). The concordance between predictions fromthe automated scoring and manual scoring was (true positive + truenegative) / (total tests) = 44/50 (88%). The fact that the automatedmethod had no false positives implies that it is effective as a first-pass screen to identify efficacious drug candidates: if it detects a hit,it is highly likely to be valid. However, if the automated algorithmdetermines a treatment group not be a hit, there is a 16% chance[(false negative) / (false negative + true negative) = 6/37] that themanual scores would have picked up the candidate.

Internal scoring consistencyTo investigate the internal consistency of each method, four serialsections of colon from study #1 (Table 1) were prepared and stainedseparately, and scored by a pathologist and the automatedalgorithm. The internal correlation of a pathologist’s raw scores forthis study was 0.929±0.067, whereas that for the automatedalgorithm was 0.903±0.040.


Automated pathology scoring in DSS colitis RESOURCE ARTICLE

Fig. 1. Evaluation of raw scores of control groups in historical studies. Themean and standard deviations of the manual (A) and automated (B) raw scoresfor equivalent (Ø, –ve and +ve) control groups in each study. The bars indicatethe mean score within the group, whereas the error bars indicate 1 s.d. Themean score across all studies for the Ø control groups divided by the meanscore of the animals in each corresponding –ve control group is 0.005±0.016(manual) and 0.430±0.098 (automated). The corresponding values for the –vecontrol group are 1±0.186 (manual) and 1±0.178 (automated). Finally, withinthe +ve control group, the corresponding values are 0.506±0.088 (manual)and 0.584±0.160 (automated). The relatively large s.d. of mean scores fromequivalent control groups suggest that the raw scores from different studiesneed to be normalized in order to be comparable.

Fig. 2. Results for study #7. Each marker indicates a single animal, and iscolor-coded by whether it was a normalized manual score (blue square) ornormalized automated score (red triangle). The markers are plotted for allanimals in each experimental test group. Means are indicated by small circles,and error bars correspond to 1 s.d. The dose of IL-22 Fc for each titration groupis also indicated.

Table 2. Results from study #7

Dose IL-22 Fc (μg/mouse)

Parameter 30 6 1.25 0.25 0.05

Manual P-value 1.79×10−5 0.0124 0.619 0.0281 0.196

Manual effect size 0.4242 0.2879 0.0303 0.1818 0.1515

Automated P-value 4.64×10−4 0.0211 0.0656 0.0927 0.116

Automated effect size 0.687 0.3946 0.2858 0.262 0.2755

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Meta-analysisIn order to better understand the general differences between theautomated and manual scoring methods, normalized scores werepooled from multiple studies. Automated scores in all studies fromthe three control groups were linearly related to manual scores ofboth pathologists (Fig. 3A), with similar linear fits. Normalizationis crucial because one pathologist assigned systematically higherabsolute scores than the other (data not shown), but thisdiscrepancy disappears with normalization. The Pearson’scorrelation coefficient between the pooled manual and automatedscores was 0.779. Correlation coefficients were also calculatedwithin each study (Fig. 3B) and were similar.

We next compared the effect sizes of treatment groups computedfrom all drug studies, using the normalized manual and automatedscores. The effect sizes were linearly related, with a correlationcoefficient of 0.830 (Fig. 3C). Interestingly, the automated scoringhad a tendency to exaggerate the effect size.

The statistical differences between the groups were also compared,by calculating P-values from a Student’s t-test between the –vecontrol and test groups from all drug treatment studies. The P-valuesobtained using normalized manual and automated scores appearedbroadly related (Fig. 3D). However, the P-values were generally higherfor the automated scores compared with the manual scores.

Correlating tissue morphology change with inflammatory cellinfiltrationThe analysis so far indicated that the automated scoring method,although slightly less sensitive than the manual method, is able tocapture pathological tissue morphology change in a consistentmanner. Because most treatment candidates that are used in colitisdrug discovery are immunomodulatory, it is informative tounderstand their effect on the dynamics of immune cell infiltrationas well as on tissue morphology. Whereas tissue morphology is bestevaluated in H&E slides, infiltration of immune cells into the gut iseffectively visualized using IHC staining. Fine quantification of stainis difficult for a pathologist to perform by eye, but relatively simpleusing Definiens. To demonstrate the ability to compare tissuemorphology change and inflammatory cell infiltration easily usingautomation, a timecourse study was performed where animals weregiven 3% DSS and euthanized after 0, 2, 4, 6 or 8 days. H&E slideswere prepared and raw automated scores were calculated. Serialsections of the H&E slides were immunostained with F4/80(macrophage marker), CD3 (T-cell marker), MPO (neutrophilmarker) and B220 (B-cell marker). The area containing cells stainedwith each marker, relative to total tissue area, was quantified (seeMaterials and Methods, Fig. 4A-F and supplementary material Fig.S1). The stain quantification took ~30 seconds per slide per processor.


Automated pathology scoring in DSS colitisRESOURCE ARTICLE

Fig. 3. Comparisons betweennormalized manual and automatedscores. (A)Scatter plot of normalizedmanual versus automated scores fromthe three control groups, across allstudies. Two linear fits corresponding tothe two pathologists who scored thestudies are shown in green and inorange. Pathologist 1: fit: y=0.960x –0.053; Pearson’s correlation: 0.788.Pathologist 2: fit: y=1.075x – 0.159,Pearson’s correlation: 0.766. If the linearfit is applied to the combined manualscores from both pathologists, the fitequation is y=0.99x – 0.080; Pearson’scorrelation: 0.779. (B)Individual Pearson’scorrelation coefficients within studies,color-coded for those studies scored bydifferent pathologists. (C)Scaled effectsizes for corresponding normalizedautomated and manual scores in animalgroups from all studies: the differencebetween the –ve control group and testgroups are shown as black circles, andthe difference between the –ve controlgroup and the +ve control group areshown as green circles. The line indicatesa linear fit: y=1.656x – 0.189; Pearson’scorrelation: 0.830. (D)A log-log graph ofP-values for Student’s t-tests betweentest animal groups and –ve controlgroups in all studies.

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Fig. 4. Correlating tissue morphology change with inflammatory cell infiltration. (A-F)Example images from an animal given no DSS (left panels) and ananimal taken down on day 6, after 5 days of 3% DSS administration (right panels). (A)Example H&E slide. (B)Pseudo-colored automated analysis result of A,where non-analyzed area (green), muscle (pink), healthy crypt area (yellow) and pathological area (red) are indicated. (C)Original image of colon stained withmarker (here MPO). (D)Automated analysis based on C, where non-analyzed area (green), tissue area (blue) and positively stained cells (in this case neutrophils;red) are shown (see also supplementary material Fig. S1). (E,F) Examples of high-magnification regions within C and D, respectively. (G)Timecourse analysis of thearea of positive IHC staining (axis and labels on left) over relevant tissue area, with an overlay of a graph of H&E automated morphology scores (axis and labels onright). Note that the automated H&E morphology scores are not normalized, because the experiment did not contain a +ve control group as in the drug-treatment studies. Dunnett’s test was performed for each group against day 0. *P<0.05 compared with control; **P<0.01; ***P<0.001.

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A statistically significant increase in F4/80 and MPO stainingwas found concomitant with crypt loss, closely correlating to theautomated scores (Fig. 4G). Note that raw automated scores wereused because this was not a drug treatment study and no –vecontrol group was run. Late in the disease (day 8), a gradualincrease in CD3 (T cells) and B220 (B cells) was also observed.Spearman’s correlations between individual automated cryptscores and the proportion of stained area for each marker was0.84 for MPO stain (neutrophil), 0.74 for F4/80 (macrophage),0.70 for CD3 (T cells) and 0.23 for B220 (B cells). The resultsshow that the crypt morphology change was most linearlycorrelated to the change in number of neutrophils, followed bymacrophages and T-cell infiltration. It was least correlated to B-cell infiltration.

DISCUSSIONIn order to increase drug-discovery efficiency, it is desirable toscreen a large number of candidates in the shortest possible timeand at minimal cost (Paul et al., 2010). We thus developed a methodto automatically analyze H&E-stained slides of colons from micewith DSS-induced colitis to speed up drug candidate identificationand save pathologists’ time.

SpeedThe automated quantification method allowed analysis of 544 slidesin 4.5 hours, which could be run overnight. By using a largernumber of processors running Definiens in parallel, the analysis isscalable to any number of slides.

Use for drug discoveryThe automated algorithm classified 88% of tested agents in thesame way as manual scores, as effective or not effective.Importantly, all the discrepancies were false negatives – theautomated algorithm failed to identify some potential candidates.The fact that no false positives were identified indicates that thealgorithm is suitable for fast screening because, if a target isidentified, it is likely to be worth the effort to validate it. Theautomated method is not, however, appropriate for evaluation ofstudies in which subtle effects need to be measured, for exampleto examine fine differences in efficacy between two compounds.

RobustnessFor an automated algorithm to be useful, it needs to be robust toroutine variation during sample preparation. The algorithm wastherefore designed to rely less on variables that are easily affectedby H&E sample preparation, such as color, and relied mostly on‘contextual’ features. As examples of this approach, a hierarchicalclassification algorithm was used to establish regions of interest ina low-magnification image and, at high resolution, healthy cryptswere identified by geometric and neighborhood features. In fact,this strategy is similar to the human visual interpretation of imagesas composed of ‘objects’, rather than classical computer-based imageanalysis, which treats images as arrays of pixels.

Owing to the robustness of the algorithm, nine out of tenexamined studies were assessed as appropriate quality for imageanalysis, and all studies had a correlation coefficient betweenmanual and automated scores greater than 0.7. Furthermore, theautomated scores had an internal consistency of over 0.9, very

similar to that of the pathologist. The high correlation argues thatthe algorithm is highly robust to sample preparation differences.

Correlation to pathologistsBy performing meta-analysis, we also demonstrate that, across allstudies, the normalized scores from the automated algorithmcorrelates well with scores from both pathologists. The pathologistscannot be compared directly to each other, because they scoreddifferent studies. But the fact that both of their scores correlatewell to the automated scores suggests that the algorithm is assessinggeneral features that are accepted signs of pathology by multiplepathologists.

Quantification of immune cell infiltrationAlthough the predominant H&E morphology observed in DSScolitis is ulceration and crypt-cell loss, another hallmark of colitisis infiltration of immune cells into the gut, which is most effectivelyvisualized using IHC staining. In order to study the relationshipbetween crypt loss and immune-cell infiltration, serial sections ofslides used for automated crypt analysis were stained forneutrophils, macrophages, T cells and B cells. Stain intensity wasautomatically quantified. Whereas H&E morphological assessmentwas optimized to be independent of stain intensity, stain intensityis the primary readout of IHC quantification and, therefore, is bynature more sensitive to sample preparation. Any proceduralchanges in staining the slides will give inconsistent values; therefore,all slides in a study should be ideally stained at the same time. Ifsample preparation is consistent, chromogenic IHC on tissues canbe used in a highly effective quantitative manner (Taylor andLevenson, 2006; Walker, 2006).

IHC analysis showed that neutrophils and macrophages increasedconcomitantly with crypt loss, with a latent increase in B and T cells.The correlation coefficients were highest between crypt morphologyscores and staining of neutrophils and macrophages. This shows thatcrypt loss was more closely associated with infiltration of innateimmune cells. The results do not imply causation – whetherneutrophils drive damage to crypts, or vice versa – but implicate arole of neutrophils in an early stage of the disease. Our results areconsistent with immune-cell infiltration quantification using flowcytometry in DSS-induced colitis (Hall et al., 2011), but ours is a lesslaborious and faster method.

By applying IHC analysis to drug-treatment studies, it would bepossible to investigate the role of a drug candidate not only on tissuemorphology, but also on immune-cell infiltration, in a relativelyeasy, but quantitative, manner.

Understanding errors in the automated algorithmWhen the relationship between effect sizes was examined usingnormalized automated and manual scores, there was a tendencyfor the automated scores to ‘overshoot’, leading to an exaggeratedeffect size. This shows that, at the upper range of the effect size,the algorithm has no problem recognizing molecular agents ascandidates. However, higher P-values in the automated algorithmcompared with manual scores shows that the intra-group varianceis higher in the automated method, and so subtle differences withsmall effect sizes would be harder to detect. These resultsdemonstrate that, for future drug-discovery studies, it would beoptimal to administer drug candidates at the maximal feasible dose.


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The larger the effect size, the higher the chance that the candidatewould be detected by the automated algorithm.

It is unclear what exactly causes the greater ‘noise’ in the automatedalgorithm compared with analysis by pathologists, but one possibilityis that pathologists are better at assimilating a larger number ofmorphological cues in the tissue features, and assessing their relativeimportance in determining pathology severity. This effect isparticularly evident in the scoring of Ø control animals, wherepathologists scored every animal as 0, in all but one study. Thisdemonstrates the pathologists’ phenomenal ability to correctlyrecognize tissue that is within ‘normal range’, a skill that is largelyacquired through experience. Such less tangible aspects of pathologyscoring is a challenge to translate into artificial intelligence.

RecommendationsHere are recommendations for using the automated algorithms forscreening for drug candidates:

(1) Include Ø, –ve and +ve controls in all studies. The +ve controlshould be a therapeutic agent known to be effective. In our hands,IL-22 Fc was found to be a particularly suitable positive controlowing to its reproducible and robust therapeutic effect. IL-22 Fc(Ota et al., 2011) binds to the IL-22 receptor, which is restrictedto cells of epithelial origins, and is crucial for wound healing,restoration of goblet cells and mucosal repair (Sugimoto et al., 2008),as well as for mucosal defense (Zheng et al., 2008).

(2) Administer the maximal feasible doses of test agents toenhance target identification.

(3) After slide preparation, briefly quality control slides for grossdefects.

(4) Run automated H&E morphology analysis on the number ofcomputer processors appropriate for the size of the drug screen.

(5) Normalize all scores using the Ø control and –ve control,and ensure that the +ve control exhibits at least 20% effect size andstatistically significant difference compared with the –ve control.If the +ve control does not meet these criteria, it is advised to scorethe study manually.

(6) Identify other treatment groups that show a >20% effect sizeand statistical difference compared with the –ve control, andvalidate these ‘hits’ manually.

(7) If desired, perform IHC on serial sections with immune cellmarkers, run the IHC image analysis algorithm and compare theseresults with the H&E morphology analysis.

Comparison to other image analysis techniquesDigital image analysis studies, especially applied to H&E slides, arerelatively rare in the literature. Unlike quantitative IHC, whereintensities of individual image pixels can simply be added to obtainoverall stain intensity, H&E-stained slides contain complex tissuestructures that are distinguished by morphology rather than stainintensity. Definiens Developer is especially suitable as image analysisalgorithm design software because it is capable of measuring bothpixel-based intensity and analyzing properties of images as objects.

Definiens has been used in the past to analyze colon cancer(Persohn et al., 2007), but this was done by counting BrdU-positivecells in IHC, which is similar to our method to analyze lymphocyteinfiltration count. Some other studies have used textural analysisto automatically identify colon tumors in H&E slides (Hamilton etal., 1997; Esgiar et al., 2002). These papers assessed tumors by

examining textural properties (i.e. distribution of pixels) inrandomly selected areas of the colon, and not by identifying ‘crypt-like’ objects as we have done. Unlike tumor identification, colonictissue with DSS-induced pathological crypt loss is often proximalto healthy crypts and, therefore, gross textural analysis alone wasinsufficient to categorize the extent of crypt loss. However, weutilize an extension of this approach, because a part of the criteriato identify objects as crypts is standard deviation of pixel intensitieswithin the object area, a concept related to textural analysis.

Applications to genetically engineered miceThe DSS model can be readily used to evaluate genetically alteredmice, because it is effective in a variety of mouse strains and doesnot require crossing to strains that are genetically susceptible tocolitis. One useful application is in wound-healing research, whereextensive epithelial loss and ulceration associated with DSS makesit an attractive model (Cox et al., 2012; Zhang et al., 2012). Theautomated methods described here could be applied to geneticallyengineered mice in such studies, so long as appropriate controlsare included.

ConclusionsOverall, we have used Definiens Developer to analyze images in asimilar way to a pathologist’s eye. The methods described here arehighly applicable to many other morphology-based tissue scoringtechniques that pathologists perform routinely on slides stained withH&E, for which few other effective automated analysis methods exist.

MATERIALS AND METHODSAnimalsAll procedures were carried out with Institutional Animal Care andUse Committee (IACUC) approval in accordance with theinstitution’s ethical guidelines.

Colitis was induced in C57BL/6 mice (Jackson Laboratory) byadministration of 3% DSS in drinking water ad libitum for 5consecutive days, similarly as described previously (Wirtz et al.,2007). Animals were euthanized on day 8. In drug-treatmentstudies, IL-22 Fc (PRO312045) (Ota et al., 2011) or the mouse anti-Ragweed isotype-matched control antibody was administered tomice in relevant experimental groups at various doses ranging from0.05 to 200 μg/100 μl intraperitoneally at days −1, 1, 4 and 6. Inthe time-course study, no antibodies were administered.

H&E slide preparation and manual scoringColons were prepared as a ‘Swiss roll’ (Wirtz et al., 2007) and fixedin formalin. Tissues were embedded into paraffin blocks and 5-μmsections were prepared. Slides were stained with H&E and scoredby one of two experienced pathologists in four anatomical regionsof the colon: the proximal colon (PC), middle colon (MC), distalcolon (DC) and rectum (R). Each region was given a raw score basedon crypt epithelial cell loss with consideration of the extent ofinflammatory cell infiltrate, on a scale from 0 (healthy) to 5 (severediffuse colitis characterized by complete loss of colonic epithelialcells). The raw scores from each region were summed to give atotal raw colitis-severity score for each animal, which ranged from0 (least severe) to 20 (most severe). Thus, the score is a compositereadout of severity as well as extensiveness of epithelial celldegeneration and inflammation, and is a simplified form of the


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Dieleman scoring scheme (Dieleman et al., 1998). Studies were‘mock-blinded’, where the pathologist chose not to look at treatmentgroups, although this information was accessible to the pathologist.

IHC staining procedureFour serial sections of 5-μm thickness were prepared from paraffinblocks, each mounted on separate slides.

Detection of macrophages, T cells and B cellsAll steps were carried out using the DAKO Autostainer. Slides wereincubated in Target retrieval solution (DAKO S1700). Macrophageswere detected with rat anti-F4/80 monoclonal antibody (SerotecMCAP497), B cells with anti-CD45r (B220) monoclonal antibody(Pharmingen 557390) and T cells with anti-CD3 monoclonalantibody (NeoMarkers RM-9107-S). Stains were visualized usingthe VECTASTAIN Elite ABC Kit (Vector Labs PK-6101). PierceMetal Enhanced DAB (Thermo Scientific PI-34065) was used forchromogenic detection, and nuclear counterstain was performedusing Mayer’s hematoxylin.

Detection of neutrophilsAll steps were carried out using the Ventana Discovery XTAutostainer. Samples were pre-treated with Cell Conditioner CC1(Ventana 950-124), and the anti-myeloperoxidase (MPO;NeoMarkers RB-373-A) polyclonal antibody was used as a primary,and visualized using OmniMap anti-Rb HRP (Ventana 760-4311).The ChromoMap DAB kit (Ventana 760-159) was used forchromogenic detection, and nuclear counterstain was performedusing Hematoxylin II (Ventana 790-2208).

Digitization of sections, image analysis software and ITinfrastructureSlides were scanned using a Hamamatsu Nanozoomer 2.0 HTdigital slide scanner running NDP Scan software, with an OlympusUPlan SApo 0.75 NA 20× objective lens. All slides were onlyscanned in the area where specimen tissue was present. Resultingimages (0.46 μm/pixel resolution) were saved in the NDPI format,a type of JPEG, with 20% default compression, on an Isilon NASdrive. Image analysis was performed with custom-written rulesetsfor Definiens Developer software (Munich, AG), and run on up to28 CPUs at a time, on two nodes each containing 16 Intel Xeonbased logical CPUs, with 96 GB of RAM, and over 220 GB of freespace. The Ethernet network speed was 10 Gbit/second.

Historical study selectionH&E colon slides from ten historical studies, for which manualscores had already been obtained, were scanned. Each studyincluded 6 to 11 (median of 8) experimental groups, each groupcontaining 4 to 10 (median of 7) animals. Each study was visuallyinspected for gross sample preparation problems. One study wasexcluded owing to serious artifacts that made scoring challengingeven for pathologists. The nine included studies, with a total of544 samples, contained artifacts that would be expected duringroutine laboratory practice.

Morphological image analysis on H&E sectionsAn algorithm (available on request) was designed to differentiaterelevant areas of the colon for assessing colitis at low resolution,

and then at high resolution, to calculate the relative proportionsof healthy and pathological areas in H&E slides of mouse colon(Fig. 5A). Images are analyzed using the RGB (red, green and blue)spectra. Definiens denotes pixel intensities within each spectrumfrom 0 (darkest) to 255 (brightest). The term ‘brightness’ withoutspecifying a single spectrum refers to the mean pixel intensities ofred, green and blue spectra.

Low resolutionScanned images of ‘Swiss roll’ preparations of mouse colon slides(H&E) are first analyzed at low resolution (0.4×). The image issubdivided into ‘objects’ using the Definiens built-in ‘multi-resolution segmentation’ algorithm (Baatz and Schäpe, 2000). Thesegmentation method divides the image areas into smaller areasthat have similar features, in a hierarchical fashion. This procedureseparates the majority of the tissue into two distinct areas (Fig. 5B):the muscle, which is highly eosinophilic (pinkish hue), and the gutlining, where healthy animals would have intestinal crypts, whichare basophilic (purplish hue). Only the basophilic area is selectedfor further analysis (Fig. 5C). Peyer’s patches are normally occurringlymphoid nodules in the intestines, containing a large number ofB and T cells. These areas are selected by their dark basophilicstaining and removed from the analysis area. Without removal, thelymphoid regions would be mis-categorized as a tissue area showingsigns of colitis-induced inflammation. During manual assessment,the pathologist categorizes four sub-regions of the colon: rectum,distal colon, middle colon and proximal colon (Fig. 5B), and selecta ‘representative’ region from each area, which is examined at ahigher magnification. The automated algorithm was not designedto explicitly distinguish between these sub-regions; however, therelative distance from the center of the Swiss roll is used to confineanalysis within the inner area of the colon (area within a 4.55-mmradius from the center of the roll), mostly excluding the proximalregion (Fig. 5C). This is because DSS-induced colitis predominantlyaffects the rectal, middle and distal regions of the colon.

High resolutionThe entire colon tissue within the selected region (within 4.55 mmfrom the center) is too large to analyze at a high resolution at onceowing to limited computational capacity. Therefore, this region istiled in a chessboard pattern, using the Definiens algorithm‘chessboard segmentation’, and each tile (approximate size of0.8×0.8 mm) is analyzed at higher magnification (Fig. 5D).

At 10× magnification, only relevant areas (within 4.55 mm fromthe center, excluding muscle and Payer’s patches) are analyzed(Fig. 5E). Healthy crypts appear as relatively basophilic epithelialand goblet cells, neatly arranged in round or oblong rings. Whenpathological changes (such as ulcers and erosions) are present,crypts appear severely disrupted or are completely absent, so thatthe entire area appears less basophilic. Typically, a large numberof round nuclei, smaller than those of epithelial and goblet cells,are found in affected areas, indicating inflammation.

The automated algorithm assesses the loss of crypts as follows:the regions where crypts should be present in healthy animals arefirst subdivided into multiple smaller areas, of sizes correspondingto average crypt diameter (50-100 μm), using the Definiens ‘multi-resolution segmentation’ algorithm (Fig. 5F). The effect is thatcohesive structures, such as crypts, are identified and appear as



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relatively round structures. Pathological regions that appearrelatively homogenous are subdivided into areas of random shapes(compare Fig. 5E and 5F).

The classification strategy of these areas relies on a likelihoodscore, which depends on a geometrical combination ofmorphological and textural features characteristic of healthycrypts or pathological tissue, namely the overall shape (crypts areellipsoid, pathological tissue is amorphous), staining intensity(crypts tend to stain more darkly) and standard deviation of pixelswithin a region, using Definiens ‘evaluation classes’. The standarddeviation of pixel intensities is higher when a large number ofsmall dark objects are present, as is the case when many

inflammatory nuclei are concentrated in a region of tissue. Areasthat have a combination of shape, staining and standard deviationmetrics, indicating a high likelihood of belonging to crypts, areclassified as healthy (Fig. 5G, yellow), and areas withcharacteristics of ulcers or erosions are classified as pathological(Fig. 5H, red). Some areas with indeterminate features are leftuncategorized (Fig. 5H, cyan). These are often areas betweencrypts where some immune cell nuclei are visible even when theintestinal tissue is perfectly healthy (Fig. 5E) so that areas haveproperties of both healthy and pathological tissues. To improvethe classification, after defining areas that are comprised ofhealthy crypts, the probability score for a unit to be classified as



Fig. 5. Method for morphological image analysis on H&E sections. (A)The automated image analysis workflow. (B)The H&E image is scaled to 1% of theoriginal size. Each region of the colon rectum (R), distal colon (DC), middle colon (MC) and proximal colon (PC) are labeled. (C)The image is subdivided by multi-resolution segmentation algorithm (Definiens). The procedure divides the image into discrete units by grouping pixels that have common morphologicalfeatures. These units are false colored in this figure by the categories assigned to different regions of the H&E slide (note the coloring is for aiding understandingonly, no color filters are applied to the original image). The hematoxylin stain is used to separate the darkly stained units, shown in purple (intestinal regionwhere, in healthy animals, crypts would be present) from the units shown in pink (muscle). Peyer’s patches, shown in light green, are excluded. Only units nearthe geometric center of the detected tissue area are analyzed (a circular region within 4.55 mm), and the region outside of this area. (D)A ‘chessboardsegmentation’, where the image is conceptually divided into equally sized square tiles except for the boundaries of tissues where they are truncated, is used toanalyze individual areas in the colon. Classifications of the tissue units are shown here in the Definiens ‘outline’ view, and an example of an analyzed region isshown in red. (E)The individual tiles are analyzed at high magnification (10×). The false-colored pink shade indicates area that was already categorized at lowresolution as ‘muscle’. This region is pixelated in appearance due to the down-sampling (analysis at low magnification) processes in C. The relevant analysis areais shown here without any false coloring. Within this region, healthy crypts, regions between crypts and regions were ulcers are visible are labeled. (F)The firststep is a multi-resolution segmentation in the relevant region. Cyan outlines show cohesive pixels that were grouped into units. (G)A combination of pixelfeatures within the units were used to categorize some as crypts (false colored in yellow), and others as still undefined (false colored cyan). (H)Within theundefined area, units with pixel features characteristic of pathology were classified as pathological (false colored red). (I)The uncategorized region was furthercategorized depending on their proximity to neighboring regions that were already categorized (compare with H). (J)The entire classified tissue area is used tocompute the final score (see also supplementary material Fig. S3).

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pathological is decreased if it is adjacent to an area that is alreadydefined as healthy. In this way, narrow strips of tissue thatcontain some infiltrates are more likely to be classified as healthy(Fig. 5I), which is consistent with the interpretation by apathologist.

Causes of misclassificationSeveral artifacts due to sample preparation affected the automatedanalysis algorithm. These included:• unraveling or distortion of the ‘Swiss roll’, resulting in a mis-

categorization of intestinal regions• incomplete sections or tissue dropout due to sectioning defects• inclusion of non-intestinal tissue in the slide, such as the perianal

glands, which can appear similar to ‘healthy crypts’• uneven sectioning of the tissue, causing irregular staining in

various regions.Illustrative images of poor sample preparation are shown in

supplementary material Fig. S2, whereas Fig. 5B shows an exampleof a good preparation.

Automated scoring methodThe areas categorized as either healthy or pathological weresummed together for all tiles (Fig. 5J and supplementary materialFig. S3) to compute a total raw score as follows: Score=2 ×Ap/(Ap+Ah), where Ap=total area scored as pathological, andAh=total area scored as healthy. In theory, the raw score is within0 (all healthy) and 2 (all pathological), but most raw scores wereunder 1. The factor of 2 in the equation was introduced so thatmost raw scores varied between 0 and 1.

Staining area quantification on IHC sectionsIHC slides were analyzed using the RGB spectra, as described in theH&E image analysis section. Images are analyzed (available onrequest) initially at low resolution (0.2×), and ‘bright’ areas areexcluded as background. The muscle area is not separated fromcrypts for analysis but, as with the H&E-stained slides, only the regionwithin a 4.55-mm radius from the center is used for analysis. Thevery dark areas (areas with brightness less than 70% of mean tissuebrightness) are excluded because they probably correspond to lymphnodes. On the rest of the tissue area, the Definiens algorithm‘chessboard segmentation’ strategy is applied to analyze tissue regionsat high resolution (10×) tile by tile. At high magnification, stainedareas of the tissue are isolated by an intensity threshold only on theblue spectrum, using the Definiens algorithms ‘auto-threshold’followed by ‘multi-threshold segmentation’, which divides the imageinto dark-blue and bright-blue regions after determining an optimalintensity threshold. Of the objects selected as ‘dark blue’, only regionswith sizes greater than 2 μm2 and under 104 μm2 are selected, becausetiny specks are unlikely to be intact nuclei, and larger dark stains arelikely to be artifacts, such as non-specifically bound antibody. Thedensity of cells is calculated as follows: Score=Ts/Ta, where Ts=totalarea occupied by the stained areas of the tissue, and Ta=total areaoccupied by analyzed tissue, in the entire slide.

Analysis methodsStatistical methodsAll data analysis was performed using MATLAB’s (MathWorks)Statistics Toolbox.

Calculation of internal consistency of scoringConsistency for a particular scoring method was computed byapplying either the manual or automated scoring method to readthe same study four times. Instead of scoring the exact same slides,four serial sections were prepared for each and every animal in astudy, and scored independently (supplementary material Fig. S4).Then, the Pearson’s correlation coefficients between fourindependent sets of raw scores were computed. With four sets ofslides, this constitutes 4×3/2=6 comparisons. The root meansquare (RMS) and root mean square error (RMSE) of all sixcorrelation coefficients was computed to estimate the consistencyof either the manual or automated method for this study.

Score normalizationAll drug-treatment studies contained a control group given waterwithout DSS (Ø control), and mice given DSS alone or DSScombined with an inert antibody (–ve control).

The raw scores (Fig. 1) were normalized by setting the mean scoreof the Ø control group as 0, and the mean score of the –ve controlgroup as 1, by the following formula: ScoreNorm=[Scoreorig –(Scoremean(Ø))]/(Scoremean(–)–Scoremean(Ø)), where ScoreNorm=normalized individual score, Scoreorig=original individual raw score,Scoremean(Ø)=mean raw score of all animals in the Ø control group,and Scoremean(–)=mean raw score of all animals in –ve controlgroup.ACKNOWLEDGEMENTSThe authors thank Thomas Bengtsson for statistical advice and critical evaluationof the manuscript, Wenjun Ouyang for help regarding use of IL-22Fc as a positivecontrol, and the Genentech pathology core labs for all slide preparation.

COMPETING INTERESTSAll authors are employed full-time by Genentech Inc., which provided all funding.All authors hold stock options, bonds and a pension plan with Genentech.

AUTHOR CONTRIBUTIONSC.K. performed all image analysis, prepared all figures, and drafted the manuscript.S.J., J.L. and X.W. carried out experiments. J.D., L.D. and J.B. conceived and guidedexperiments. C.K. and S.G. performed statistical analysis. All authors were involvedin writing the paper and had final approval of the submitted and publishedversions.

FUNDINGThis work was funded by Genentech, Inc.

SUPPLEMENTARY MATERIALSupplementary material for this article is available athttp://dmm.biologists.org/lookup/suppl/doi:10.1242/dmm.011759/-/DC1

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Automated pathology scoring in DSS colitis RESOURCE ARTICLED

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an entirely automated method to score dss-induced colitis ...devise an entirely automated method to...

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