Journal of Hepatology 2000; 32: 453-464 Printed in Denmark All rights reserved Munksgaard . Copenhagen

Copyright 0 European Association for the Study of the Liver 2ooO

Journal of Hepatology ISSN 0168-8278

Automatic quantification of liver fibrosis design and validation of a new image analysis method: comparison with semiquantitative indexes of

fibrosis

Marco Masseroli, Trinidad Caballero, Francisco O’Valle, Raimundo M.G. Del Moral,

Alejandro Perez-Milena and Raimundo G. Del Moral

Department of Pathology, School of Medicine and University Hospital, University of Granada, Granada, Spain

BackgrounrVAims: Liver fibrosis is one of the most important and characteristic histologic alterations in progressive and chronic liver diseases Thus, in both clinical and experimental practice, it is fundamental to have a reliable and objective method for its precise quantification. Several semiquantitative scoring sys- tems have been described. All are time-consuming and produce partially subjective fibrosis evaluations that are not very precise. This paper describes the design and validation of an original image analysis-based ap plication, FibroQuant, for automatically and rapidly quantifying perisinusoidal, perivenular and portal- periportal and septal fibrosis and portal-periportal and septal morphology in liver histologic specimens. Methods: The implemented image-processing algo- rithms automatically segment interstitial fibrosis areas, while extraction of portal-periportal and septal region is carried out with an automatic algorithm and a simple interactive step. For validation, all automati- tally extracted and qua&&d.

areas Were also manually segmented

common in many progressive and F IBROSIS is chronic liver diseases and is one of the main histo-

logic features considered for their diagnosis and prog- nosis (1). Therefore, its precise and objective histologic quantification is extremely important both in in viva experimental models and in clinical practice. Until now, the histologic extent of liver fibrosis has been esti- mated mainly by conventional and modified semi- quantitative scoring systems (l-6). However, even if

Received 21 April; revised 5 August; accepted 31 August 1999

Correspondence: Marco Masseroli, Dpto. de Anatomia Patolbgica, Facultad de Medicina, Avda. de Madrid 11, 18012 Granada, Spain. Tel: 34 958 244097. Fax: 34 958 243510. e-mail: masseroli@biomed.polimi.it

Results: Statistical analysis showed significant h&a- and interoperator variability in manual segmentation of all areas. Automatic quantifications did not sign& cantly differ from mean manual evaluations of the same areas. Comparison of our image analysis quanti- fications with staging histologic evaluations of liver fibrosis showed sign&ant correlations (Spearman’s, 0.72<rC0.83; p<O.OOOl) and that the latter are based more on the distribution patterns than on the quantity of fibrosis. Conclusions: FibroQuant is a sensitive, precise, objec- tive and reproducible method of fibrosis quantifl- cation, which complements semiquantitative histo- logic evaluation systems. This novel tool could be of special value in clinical trials and for improving the prognosis and follow-up among patients with fibrosis- inducing hepatic diseases

Key words: Automatic cessing; Liver fibrosis; metry.

these semi-quantitative

quantification; Image pro- Portal-periportal morpho-

methods describe pathologic patterns of the hepatic structure,

well the they pro-

duce fibrosis evaluations that are not very precise and, especially at intermediate grades, are subjectively de- pendent on the visual interpretation of the observer, who must be an experienced pathologist (5-7).

In recent years, quantitative evaluations through im- age analysis have been utilized (6,8-10). Digital image analysis rapidly provides objective quantitative results similar to but more precise than those determined by semi-quantitative scoring methods, without requiring the presence of an experienced pathologist (11). How- ever, due to the lack of specific automatic applications, the use of image analysis has been limited to quantifi- cation on the entire image of areas densitometrically

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segmented according to their staining intensity In this way, only global information on the imaged field can be obtained, and a more accurate analysis of individual structural components cannot be performed. In short, a perfect method for morphologically quantifying he- patic fibrosis has yet to be described (12). Falling costs ’

and improvements in electronic and computer tech- nology, alongside the biomedical application of auto- matic image segmentation and object classification techniques, have enabled the implementation on simple personal computers of specific automatic applications for routine histologic morphometry and fibrosis quantification (13).

This paper describes the design and validation of an original image analysis-based application for auto- matically quantifying liver fibrosis and the morphology of the portal-periportal and septal areas. Our method facilitates rapid, reproducible and sensitive quantifi- cations of perisinusoidal, perivenular, and portal-peri- portal and septal fibrosis separately in hepatic tissue specimens. Thus, this application represents an import- ant complement to semi-quantitative scoring systems, aiding a better evaluation and understanding of in vivo experimental models of hepatic diseases and a more accurate determination of the disease stage, essential for a correct prognosis.

Materials and Methods Histologic material Liver needle biopsy specimens from 59 subjects (11 normal and 48 diagnosed with different grades of chronic viral hepatitis C activity), who gave their written informed consent, were fixed in buffered 4% formalin, embedded in paraffin and serially sectioned at 4 p thick- ness. Afterwards, they were stained with routine dyes (hematoxylin- eosin, reticulin, Gomori’s trichromic, Perls and PAS-diastase) for con- ventional histopathologic evaluation, and with 1% picro Sirius red F3BA (Gurr, BDH Chemicals Ltd., Poole, United Kingdom) for im- age analysis quantification. In the latter case, to improve staining, tissue sections were kept after deparaffination for 3-5 days in 70% alcohol as mordent. Picro Sirius red stains connective fibers deep red and cell nuclei and cytoplasmatic structures light redlbright yellow (14).

Image analysts system The system used consisted of a black and white Vidamax CCD BCD- 700 video camera (Microptic S.C.P., Barcelona, Spain) coupled to an Olympus BH-2 microscope with an Olympus MTV-3 adapter (Olympus Optical Company, Ltd., Tokyo, Japan) and CoMected to a compatible PC with a Pentium-S 100 MHz processor (Intel Corpor- ation, Santa Clara, California, USA), 16 Mbyte RAM, 1 Gbyte hard disk, S-VGA graphics card, MVP-AT card (Matrox Electronic Sys- tems, Ltd., Dorval, Canada) with 1 Mbyte RAM for image acqui- sition and processing, RGB high resolution monitor GVM-1411QM (Sony Corporation, Tokyo, Japan) and a 17 inch RGB MF-5117 monitor (Iiyama Electric Corporation Company, Tokyo, Japan).

Image processing and computer operations were performed on MS-DOS 6.20 (Microsoft Corporation, Redmond, Washington, USA) in a MS-Windows 3.11 environment (Microsoft), through orig- inal algorithms implemented using Visilog 4.1 software (Noesis S.A., Velizy, France) for development of image analysis programs.

Image processing method To automatically quantify perisinusoidal, perivenular and portal-pcri- portal and septal fibrosis and portal-periportal and septal mor- phology on liver histologic sections, we developed several image pro- cessing algorithms that have been brought together in one image analysis application, named FibroQuant. Fig. 1 shows the main com- ponents of the application, which is implemented in three distinct parts: 1) acquisition and preprocessing of the digital image, 2) pro- cessing of the preprocessed image, 3) analysis of the processed image and measurement of parameters.

l Image acquisition and preprocessing Histologic images of liver biopsies were digitized in black and white at 8 bit intensity resolution (256 gray levels) with a global magnifi- cation of 200X. Optical image size was of 44 221.34 mz for a 256X256 square pixel image, resulting in an optical resolution of 0.67 ,um*/pixel. The analog images were acquired using an IF 550 green optical filter with illumination intensity values slightly above those used for normal observation. This procedure reduces the influence in the digital image of elements not specitically stained by picro Sirius red and of no interest for the evaluation (such as hepatocyte cell cyto- plasm and nuclei contrasted with pi& acid) without modifying the stained fibrotic areas (Fig. 2A), thus yielding better results in the sub- sequent automatic segmentation of fibrosis areas.

To correct potential variability in the staining intensity of tissue sections from different staining batches, all image acquisition par- ameters were fixed and the acquisition illumination intensity was cali- brated by the user adjusting only the microscope condenser aperture. To optimize the setting and reproducibility of acquisition illumination

ACQUISITION AND

Analog QUANTIFICATION SET-UP

microscopic JI image IMAGE 1 A ' DIGITIZATION Image

& acquisition

and SHADING CORRECTION preprocessing AND NORMALIZATION

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PORTAL-PERIPORTAL AND SEPTAL AREA, AND 1 PORTAL LUMEN AREA QUANTIPICATION Image

analysis

DATA STORAGE AND EXPORT 1

Fig. 1. Flow chart of the main components of FibroQuant, an image analysis application for the automatic quantiJi- cation of liver fibrosis and morphology of portal-periportal and septal area.

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Liver$brosis quantification by image analysis

intensity values, an image acquisition control function was developed. This function shows the user the acquired digital image with the result of its automatic thmsholding superimposed on it.

To avoid interference caused by irregular lighting and camera lens aberrations, the shading in the digital image is corrected by pixel-to- pixel comparison with a background image previously acquired under the same focus and illumination conditions on a clear tissue-free part of the same analyzed slide. The corrected image is then normalized by expanding its gray level histogram to the full range of available levels (15).

l Image processing Fibrosis area segmentation. Automatic thresholding of the areas specifically stained with Sirius red is done using the global threshold- ing method defined by Kurita et al. (16). In images of liver tissue sections stained with Sirius red, acquired with excess light and pmpro- cessed as previously described, a binary image is yielded that repre- sents the different fibrosis areas in the image. These are then extracted and classified according to the region in which they are set (Fig. 2B and C).

Portal-periportal and septal area extraction. Fibrosis agglomerations are considered for extracting the portal-periportal and septal area in the image. Initially, the binary image of thresholded areas speciticahy stained by Sirius red is automatically processed by a sequence of mathematical morphology transformations extracting all possible re- gions of interest in the image (17,18). Then, the portal-p&portal and septal area is identi6ed interactively among the extracted regions simply by drawing a polygonal line over it (Fig. 2D). Occasionally, especially in histologic images of pathologic liver biopsies, when the external borders of the portal-periportal region are not well delined, the contour of the automatically extracted area may not exactly match the perimeter of the portal-periportal region. In such cases the same polyline can be used to correct the contour of the identified area.

Portal vessel and biliary duct lumen segmentation. The most important vessel and biliary duct lumina inside the portal region are automati- cally segmented through automatic thresholding, size filtering and Boolean operations. Automatic thresholding is performed by apply- ing the global method of Kittler & Illingworth (19) to the clear areas of the image, which complement the regions that were previously seg- mented by Ku&a’s thresholding. The Kittler and Illingworth’s auto- matic thmsholding done in this way segments image areas that are. very lightly or not at ah stained. Of these areas, those inside the portal-periportal region and wider than 160 wz are then automati- cally isolated by size filtering and Boolean operations (Fig. 2E). When zones of tissue disruption are included in the image, they may present the same densitometric and dimensional features as do vessel and biliary duct lumina. Therefore, lumen areas are interactively identified among the automatically segmented regions by drawing a polygonal line over them. Moreover, when vessel lumina are Glled with hematic cells and protein material, their area may be either not automatically extracted or only partially uncorrected. In such cases, the lumen areas can be interactively segmented or partially corrected by using the same polyline.

l Image analysis In this section, perisinusoidal fibrosis areas (SF), portal-periportal and septal fibrosis areas (PF), portal-periportal and septal area (PA), and portal vessel and biliary duct lumen areas (LA) are automaticahy quantified in ,mn’ and shown superimposed on the initial prepro- cessed image. Percentage arca values are also calculated, as follows:

- percentage ama of perisinusoidal fibrosis (PSF): PSF=lOO*SF/(IA-PA), where IA is the area of the whole image;

- percentage area of portal-periportal and septal fibrosis (PPF): PPF=lOO*PF/(_PA-LA);

- percentage area of portal vessel and biliary duct hunina (PLA): PLA= lOO+LAiPA.

Fig. 2. Areas automatically quantified by FibroQuant, an image analysis application for the automatic quanttfication of liver fibrosis and morphology of portal-periportal and septal area. A: digital image of liver tissue section stained with picro Sirius red. B: perisinusoidal fibrosis areas. C: portal-periportal fibrosis areas. D: portal-periportal area, E: most significant portal vessel lumina of image A. Bar: 35 pm.

Quality control To determine the objectivity, reproducibility and robustness of the image analysis method, its main parts were analyzed, testing:

- automatic segmentation of fibrosis areas; - extraction of portal-periportal and septal area; - automatic segmentation of portal vessel and biliary duct hnuina; - robustness of the algorithm outputs to slight deviations in the

input.

l Automatic segmentation ofjbrosis areas Perisinusoidal and portal-p&portal and septal fibrosis areas of 15 images of normal liver and 15 with different degrees of periportal fibrosis, including all or a portion of a portal tract, were manually thresholded 5 times on 5 different days by five independent operators. Of these IIve operators, two were pathologists experienced in the vis- ual evaluation of liver fibrosis and three were. trained in manual image segmentation. Resulting PSF and PPF values were statistically evalu- ated to analyze the intra- and interoperator variability of manual seg- mentation. Moreover, for each image the means of the results gener-

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Fig. 3. Distribution of quantification values of areas manually segmented byJive operators duringJive subsequent replications on 30 different images. A, C,E,G: normal liver images; B,D,F, H: pathologic liver images; PSF: percentage perisinusoidal fibrosis area; PPF: percentage portal-periportal and septal$brosis area; PA: portal-periportal and septal area; LA: portal vessel and biliary duct lumen areas; ANOVA2: two-way ANOVA; NS: not significant.

456

ated by all operators in their multiple evaluations ~nere calculated to determine reference values for mean manual thresholding. To assess the accuracy of the proposed automatic method, these reference values were statistically compared to the percentage values of fibrosis areas automatically thresholded on the same images.

l Extraction of portal-periportal and septal area Portal-p&portal and septal areas present in the 30 images considered for the validation of fibrosis area thresholding were automatically seg- mented and interactively identified by the same five independent oper- ators 5 times on 5 different days. In each evaluation all operators could modify the area contours according to their personal judge- ment, by using the method previously illustrated. Resulting PA values were statistically analyzed to determine their intra- and interoperator

Liver fibrosis quanttjication by image analysis

variability. Moreover, for each image the mean of the results gener- ated by all operators in their multiple evaluations was calculated to defme a reference value for mean manual extraction. To evaluate the precision of automatically segmented PA, these reference values were statistically compared to the values of the same areas automatically extracted and interactively identified with no modification.

9 Automatic segmentation of portal vessel and biliary duct lumina To evaluate the automatic segmentation of the most significant portal vessel and biliary duct lumina, we used the same 30 images as in the validation of fibrosis area thresholding and PA extraction. All vessel and biliary duct lumina located inside the identified PA and wider than 160 pm* were automatically segmented, interactively identified and, when necessary, modified by the same five independent operators

TABLE 1

‘Rvo-way ANOVA results of quanti6cation values of manually segmented areas by independent operators (Oper) during five subsequent replications

Norm Inter Intra Interact Path Inter Intra Interact

PSF 5 Oper p<0.0001 NS p<O.OOOl 5 Oper p<O.OOOl p<0.0001 p<0.0001 3 Oper p=O.O018 p=o.O015 p<0.0001 3 Oper NS p=O.O230 p<O.OOOl 2 Oper p<0.0001 p=O.O226 p=o.O005 2qPer p=o.O004 p=o.O012 NS

PPF 5 Oper p<O.OOOl NS p<O.OOOl 5Wr p<O.OOOl p<O.OOOl p<O.OOOl 3 Oper p=O.O018 p=o.O005 p<0.0001 3 Oper NS p=O.O418 p<O.OOOl

2Oper p<O.OOol NS p=o.O003 2Oper p=O.OOOl p=o.O005 NS PA 5 Oper NS NS p=O.O284 5 Oper p=O.O036 p=o.O439 NS

4 Oper NS NS NS 4 Oper NS NS NS LA 5oPer NS NS NS 5oPer NS NS NS

Norm: normal liver images (n= 15); Inter: interoperator differences; Intra: intraoperator variability; Interact: interaction between operators and replications; Path: pathologic liver images (n=15); PSF: percentage perisinusoidal fibrosis area; PPF: percentage portal-periportal and septal fibrosis area; PA: portal-periportal and septal area; LA: portal vessel and biliary duct lumen areas; NS: not significant.

TABLE 2

Mean and standard deviation quanti6cation values and statistical analyses of areas manually segmented (manual) and automatically extracted (Auto) by independent operators (Oper)

Norm Manual Auto Student’s t-test Path Manual Auto Student’s t-test

PSF (%) 5 Oper 1.9120.85 2.822 1.70 NS 5oPer 2.8921.48 4.7622.70 p=O.O290 3 Oper 2.7321.32 2.822 1.70 NS 3 Oper 4.1821.95 4.7622.70 NS 2 Oper 0.68kO.32 2.82k1.70 p<O.OOOl 2 Oper 0.9520.84 4.7622.70 p<O.OOOl

PPF (%) 5 Oper 52.05k9.67 59.79k9.38 p=O.O329 5 Oper 41.52k4.94 52.3256.03 p<O.OOOl

3oPer 57.47k8.71 59.7929.38 NS 3 Oper 49.3825.26 52.32k6.03 NS 2 Oper 43.91kl1.33 59.7929.38 p=o.O005 2 Oper 29.6825.54 52.3226.03 p<0.0001

PA (pm*) 5 Oper 18534.02’7117.46 18296.8126886.11 NS 5 Oper 18675.7127018.27 18746.34k6582.15 NS 4 Oper 18484.16Z7086.18 18296.81Z6886.11 NS 4 Oper 18513.6647014.92 18746.34k6582.15 NS

LAW*) 5 Oper 1813.7421284.89 1603.55+1313.88 NS 5 Oper 1797.1841913.84 1418.7621955.07 NS

Norm: normal liver images (n= 15); Path: pathologic liver images (n= 15); PSF: percentage perisinusoidal fibrosis area; PPF: percentage portal- periportal and septal fibrosis area; PA: portal-periportal and septal area; LA: portal vessel and biliary duct lumen areas; NS: not significant.

TABLE 3

Correlations and agreements between different classifications

FS FK AUPF AUPA AUPAF

FS - Pa=93.22%, kappa=0.90 Pa=33.90%, kappa=0.12 Pa=40.68%, kappa=0.21 Pa=37.29%, kappa=0.17 FK r=0.94, p<O.OOOl - Pa=35.59%, kappa=0.14 Pa=45.76%, kappa=0.27 Pa=38.98%, kappa=0.19 AUPF r=0.69, p<O.OOOl r=0.71, p<O.OOOl - Pa=81.35%, kappa=0.70 Pa=96.61%, kappa=0.94 AUPA r=0.81, p<O.OOOl r=0.78, p<O.OOOl r=0.87, p<O.OOOl - Pa=84.74%, kappa=0.75 AUPAF r=0.73, p<O.OOOl r=0.75, p<O.OOOl r=0.98, p<0.0001 r=0.89, p<O.OOOl -

In each comparison, 59 cases were considered. FS: Scheuer fibrosis staging classification; FK: Knodell fibrosis scoring classification; AUPF: IC- means clustering classification based on mean portal-periportal and septal fibrosis values per portal tract (MPF); AUPA K-means clustering classification based on mean portal-p&portal and septal area values (MPA); AUPAF: K-means clustering classification based on both MPF and MPA values; r: Spearman’s correlation coefficient; Paz percentage agreement.

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according to their personal judgement, 5 times on 5 different days. Resulting LA values were statistically evaluated to analyze their intra- and interoperator variability. Moreover, for each image the mean of the results generated by all operators in their multiple evaluations was calculated to determine a reference value for mean manual segmen- tation. To assess the accuracy of the proposed automatic method, vessel and biliary duct lumen areas obtained as a result of the auto- matic processing of the same images and interactively identified with no modification were statistically compared to these manual segrnen- tation reference values.

l Robustness of the algorithm outputs Robustness of the algorithms to slight deviations in the input was experimentally evaluated on a test image of normal liver and on an image with increased periportal fibrosis. For each test image, a se- quence of images with slight progressive variations in gray level distri- bution was created, blurring the initial image by convolving it with Gaussian point spread functions of increasing standard deviations in the range 0.0-4.0 (15). All images were automatically analyzed with the algorithms. The quantified values of PSF and PPF were statisti- cally compared with the Gaussian standard deviations used to per- turb the images, and Pearson’s correlation coefficients were calcu- lated. Only Gaussian standard deviations up to 2.0 were considered for the correlation of PSF values, because greater perturbations tot- ally blurred the thin shape of perisinusoidal fibrosis in normal liver.

Histologic evaluation Analysis of liver biopsies was carried out with both conventional histopathologic evaluation and automatic image analysis quantifi- cation using the FibroQuant program. Conventional histologic evalu- ation was done by scoring the fibrosis in each biopsy, using subindex four of the activity index of Knodell (FK) (2) and the staging system of Scheuer (FS) (3). The FK system uses a scale of four stages, where score 0 represents absence of fibrosis, score 1 fibrous portal expan- sion, score 3 bridging fibrosis (portal-portal or portal-central link- age), and score 4 cirrhosis. The FS system instead uses a scale of 5 stages, where stage 0 corresponds to score 0 of FK, stage 1 represents enlarged, fibrotic portal tracts; stage 2 periportal or portal-portal septa, but intact architecture; stage 3 fibrosis with architectural distor- tion, but no obvious cirrhosis; and stage 4 probable or definite cir- rhosis. Image analysis quantification was performed by analyzing in each biopsy the six most altered portal tracts, if present, and by con- sidering the mean values per portal tract of PF (MPF), PPF (MPPF), PA (MPA) and LA (MLA). Subcapsular portal tracts and biopsies with less than 4 portal tracts or very fragmented were not included in the quantification. The K-means clustering technique was used to define three new automatic classifications for the evaluated histologic samples, based on the quantified values of MPF and MPA separately or in conjunction (AUPF, AUPA and AUPAF, respectively). In all three classifications, four classes were determined in order to easily compare the results against the classification given by the FK scoring.

Statistical analysis Data were analyzed with BMDP software (University of California, Los Angeles, California, USA). Before each statistical test, normality of the variable was verified with the W normality test of Shapiro and Wilk. When the variable did not show a Gaussian distribution, normality was ensured through logarithmic transformation of data.

To evaluate the intra- and interoperator variability in manual seg- mentation of the areas of interest in the image, statistical analyses were carried out on percentage area values using two-way ANOVA with replication and operator as factors, followed by Tukey’s tests for comparison of multiple groups. To analyze the accuracy of the pro- posed method in the automatic segmentation of the same areas, the reference values of mean manual segmentation were statistically com- pared with the results of the implemented automatic algorithms by using Student’s t-tests.

To compare the fibrosis evaluations by conventional histologic analyses with those by FibroQuant quantifications, correlations and agreements between FK, FS, AUPF, AUPA and AUPAF class&

Liver jbrosis quant$cation by image analysis

Results Quality control

l Automatic segmentation of fibrosis areas

The distributions of PSF and PPF values of manually segmented areas in normal and pathologic liver images are illustrated in Fig. 3A-D. The results of the statisti-

cations were determined using Spearman’s rank test, percentage agreement (Pa) and kappa coefficient (20). One-way ANOVA with FK, FS, AUPF, AUPA or AUPAF as factors, followed by Newman- Keuls’s tests for comparison of multiple groups, were performed on MPF, MPPF, MPA and MLA values. Besides, correlations between these values and all the classifications determined were analyzed using Spearman’s rank test. In all tests differences were considered signifi- cant when pCO.05.

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fibrosis per portal tract (MPF) . *, 7, §, #, $: Newman-Keuls’ test signtjicant comparisons; *: pcO.01 with stage 0, score 0 or class 1; t: ~~0.01 with stage 1, score 1 or class 2; I: $: pCO.05 with stage 2.

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cal analyses, summarized in Table 1, showed significant lower mean quantifications than those performed by differences between all five operators, for both normal the other three operators (Tukey’s test, ~~0.01 for PSF and pathologic liver images, and significant intraoper- and PPF, both in normal and pathologic liver images). ator variability for pathologic images. Moreover, the Therefore, we only considered the evaluations of the two operators not trained in manual image segmen- three best-trained operators to define the reference tation (OPERl and OPERS) presented significantly values for mean manual thresholding of liver fibrotic

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Liver fibrosis quantification by image analysis

areas (Table 1). Statistical comparisons of PSF and PPF values obtained by automatic thresholding with the reference values of mean manual thresholding showed no significant differences, either in normal or in pathologic liver images (Student’s t-test, p>O.l for all comparisons) (Table 2).

Extraction of portal-periportal and septal area Statistical analysis of the quantification results of inter- actively identified and modified PA showed significant differences for pathologic liver images (Table 1 and Fig. 3E and F). Differences were mainly caused by one operator who modified the automatically segmented areas substantially more than the other operators, en- larging the extracted periportal areas, whereas the other operators changed the contours of the automatic segmentations only when these were clearly uncorrec- ted. Thus, to define a reference value for the mean manual segmentation of PA, we considered only the areas extracted by the four operators who did not sig- nificantly differ (Table 1). Comparisons of the refer- ence mean quantification values of these areas with those of the same areas automatically segmented and interactively identified with no modification showed no significant statistical differences, either in normal or in pathologic liver images (Student’s t-test, ~~0.1 for both comparisons) (Table 2).

l Automatic segmentation of portal vessel and biliary duct lumina

Statistical analysis of interactively identified and modi- fied lumen areas showed no significant intra- and inter- operator differences, either in normal or pathologic liver images (Table 1 and Fig. 3G and H). Lumen areas obtained with the designed automatic algorithm did not significantly differ from reference values for mean manual segmentation (Student’s t-test, p>O.l both in normal and pathologic liver images) (Table 2).

l Robustness of the algorithm outputs Statistical analysis of the automatic quantifications of images increasingly blurred with Gaussian point spread functions showed strong correlations of PSF and PPF values with the standard deviations of Gaussian functions used to perturb the images (data not shown). Values of Pearson’s correlation coefficients ranged from -0.981 to -0.944 for PSF and from 0.995 to 0.941 for PPF in normal and pathologic liver im- ages, respectively.

Histologic evaluation Conventional semi-quantitative FS and FK evalu- ations presented a highly significant correlation be-

tween them (Spearman’s, r=0.94; p<O.OOOl) and good significant correlations with AUPF, AUPA and AU- PAF classifications (Table 3). Agreement was strong between the FS and FK determinations (Pa=93.22%; kappa=0.90) and weaker between these two and the AUPF, AUPA and AUPAF classifications (Table 3).

Values of MPF and MPA showed good significant correlations with FS and FK scores (Spearman’s, 0.72CrC0.83; p<O.OOOl) (Table 4 and Fig. 4A-D), whereas correlations of MPPF and MLA with FS and FK were also significant but weaker (Table 4). More- over, MPF MPPF and MPA showed statistically sig- nificant differences in biopsies with different FS or FK scores (ANOVA, p<O.OOOl), whereas MLA values did not significantly differ (Table 4 and Fig. 5A-D). Simi- lar results were obtained considering AUPF AUPA and AUPAF classifications, with AUPAF showing better correlation with the quantitative histologic values and better separation between classes (Table 4 and Fig. 4E, F and 5E, F).

Discussion Hepatic fibrosis is one of the most frequent lesions seen in chronic liver disease. Its histologic appearance varies according to the etiology and natural evolution of the disease and its extent is an important diagnostic and prognostic parameter (1). Therefore, it is clearly funda- mental to have a reproducible, objective and rapid method for precisely quantifying the degree of hepatic fibrosis in the different compartments of the liver struc- ture. Semi-quantitative scoring methods have been widely used to evaluate hepatic fibrosis (l-4). However, in spite of attempts to improve their characterization and objectivity by introducing new scores (5,6), some important limitations and pitfalls remain. First, while they remain valuable in describing the localization and pattern of hepatic lesions, they are to a certain extent subjective. Second, semi-quantitative scores are insuf- ficient for precise quantifications and not sensitive enough to detect small changes in fibrosis (5,7). Third, they include some ambiguous terms and still need to be more clearly defined (12). Finally, the use of different scoring scales, even if there are only slight modifi- cations between them, and the definition of different histologic stages of fibrosis, hamper comparisons be- tween results of different authors.

A calorimetric method was proposed for objective global quantification of total collagen in a tissue sec- tion (21,22). However, since tissue morphology is not considered during the quantification, more accurate and selective evaluations of collagen content in the dif- ferent liver regions are not possible. More recently, digital image analysis quantifications have been intro-

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duced (6,8-10). Computerized image analysis rapidly supplies objective and precise quantifications of liver fibrosis that correlate well with calorimetric methods and correlate with serum markers of fibrosis better than do evaluations obtained by semi-quantitative scoring methods (11,23). However, the absence of spe- cific image processing applications has limited the use of image analysis to the quantification, on the entire image, of areas densitometrically segmented according to their staining intensity. Separate evaluations of per- isinusoidal and portal fibrosis were achieved by draw- ing the contour of the portal region manually on the image or by using images acquired with different mag- nifications (6,8). The first method can easily give sub- jectively different quantifications due to the difficulty of identifying the limits of the portal areas in patho- logic livers, whereas the second method produces por- tal fibrosis quantifications lacking in resolution and very likely missing information on periportal fibrosis.

The method herein described enables the separate quantification on a single liver histologic image of per- isinusoidal, perivenular, and portal-periportal and septal fibrosis, and portal-periportal and septal mor- phology. Statistical validation demonstrated the accu- racy of the automatic segmentation of fibrosis areas in comparison with reference mean manual segmentation by trained operators, and that the manual procedure produces statistically different intra- and interoperator results. The automatic extraction of PA and the magni- fication used allow the reproducible quantification of periportal fibrosis, very important in the prognostic evaluation of many hepatic diseases. Using earlier im- age analysis methods, periportal fibrosis extending into the lobule from disruptions of the external limiting portal membrane could not be quantified separately from perisinusoidal fibrosis, due to the difficulty of their objective and reproducible segmentation. The im- plemented automatic algorithms extract PA statisti- cally similar to those segmented by trained operators, ensuring the accuracy and complete objectivity and re- producibility of results.

Extraction of the most important portal vessel and biliary duct lumina enables even more precise quanti- fications of percentage portal-periportal and septal fi- brosis by subtracting their areas from PA determi- nations, and simultaneously allows the quantification of the modifications of lumen areas. The statistical validation also showed the accuracy of the automatic segmentation of these portal vessel and biliary duct lu- mina. The isolation and preservation of the different morphologic structures permits their absolute quanti- fication in metrical units and the calculation of the in- dividual percentage relations between them.

Simulation of slight modifications of image staining showed a strong correlation of automatic quantifi- cations of fibrosis with the degree of image pertur- bation. PSF and PPF values were distributed in an al- most linear way, with small oscillations that were mainly due to the automatic thresholding algorithm. These fluctuations produced differences in histologic quantifications but these were within the precision lim- its of available tissue sectioning and staining tech- niques. These results demonstrate the great robustness of our quantification approach.

Although we used specific software (Visilog 4.1) to develop our image analysis application, the principles and solutions suggested herein are applicable else- where. Similarly, Sirius red staining was chosen for its reproducibility and stability and its virtual absence of artifacts and background (24). Such characteristics make this total tissue collagen dye recommended for routine quantifications of liver fibrosis (25). However, FibroQuant can also quantify features in tissue speci- mens stained with immunohistochemical methods for interstitial molecules (e.g. collagens, laminin, fibronec- tin). The automatic quantification of these stainings, made more reproducible and homogeneous by the use of automatic immunostainers, would enable accurate and objective evaluations of specific tissue compo- nents.

The lack of a standard for tissue staining intensity prevents the automatic normalization of image acqui- sition illumination according to staining intensity, so that any possible batch differences in staining intensity can be compensated only by the interactive tuning of image acquisition illumination. This practice can lead to inter-assay differences that bias the reproducibility and objectivity of quantifications. Digital image nor- malization and the use of our acquisition control func- tion, described above in Materials and Methods, en- sure the total intraoperator reproducibility of image acquisition illumination settings (13).

The quantifications performed by FibroQuant are rapid: with the hardware configuration described in the Materials and Methods section, mean image quantifi- cation time varies from 35 s/image to 95 s/image, de- pending on the morphologic complexity of the image. Nevertheless, due to the magnification used and the number of portal tracts to be analyzed for a correct biopsy evaluation, the method herein presented and image analysis methods in general still need more evaluation time per biopsy than do semi-quantitative methods. However, the advantages of producing accu- rate, objective and reproducible results without requir- ing the presence of an experienced pathologist make our method an essential complement to semi-quanti-

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tative evaluations when a precise quantification of liver fibrosis is required.

Comparison of image analysis quantifications with conventional semi-quantitative histologic evaluations of liver fibrosis showed that MPF and MPA par- ameters by FibroQuant presented good significant cor- relations with Knodell and Scheuer determinations that were almost as strong as the correlation between the Knodell and Scheuer evaluations themselves. Moreover, MPF and MPA presented mean values with significant differences between the classes defined by Knodell and Scheuer indexes. Nevertheless, as clearly shown in Fig. 4 and 5, besides these differences in means, each class defined by Knodell or Scheuer indexes presented a great dispersion of MPF and MPA values, and biopsies with different FK and FS stages had similar quantitative histologic values. Moreover, classifications based on MPF and MPA agreed only poorly with Knodell and Scheuer classifications (33.90% <Pa <45.76%; 0.12 <kappa <0.27). These re- sults demonstrate that conventional semi-quantitative evaluations are based on the distribution patterns of fibrosis more than on its quantity and that during the semi-quantitative evaluations a visual integration is performed, so that it is the extension of the portal- periportal and septal regions that is considered instead of the fibrosis present in these areas. In fact, the best correlations with semi-quantitative evaluations were obtained with the MPA values and the AUPAF classi- fication. Moreover, the semi-quantitative evaluations generally focus on the most pathologic pattern, even if its occurrence is very limited within the biopsy, and portal regions presenting different pathologic grades can often appear in a single biopsy All these elements determine the not very high correlations between the parameters quantified by FibroQuant and the Knodell or Scheuer determinations, and also explain the vari- ability of image analysis quantifications within the classes defined by the semi-quantitative evaluations.

The fact that the Knodell and Scheuer indexes de- scribe the most pathologic distribution patterns of fi- brosis, and not its real quantity within a biopsy, raises the important question of which of these items is more important in terms of clinical prognosis. Now that we have an objective method to precisely quantify the ex- tent of fibrosis, clinical trials of repeated biopsies can be done to correctly answer this question. Based on our experience and preliminary results (data not shown), we think that both the distribution pattern and the exact quantity of fibrosis are important for the best diagnosis of lesion stage, the most accurate prognosis, and the correct evaluation of different phar- macologic treatments. Thus, semi-quantitative histo-

logic evaluations of the fibrosis distribution pattern can be well complemented by the sensitive, precise and objective quantifications of fibrosis extent provided by the image analysis method described herein. Chrono- logical follow-up studies demonstrated that the in- crement of hepatic fibrosis with age is not even and gradual, and may remain below 5% for a long period of time (10). Therefore, we agree with Pilette and co- workers (11) in recommending the use of the image analysis technique in clinical trials and when the vari- ation in liver fibrosis must be precisely quantified, since only image analysis can detect moderate changes in liver fibrosis (12). However, image analysis can produce reliable quantifications only when the sample specimen is adequate. A liver biopsy that is too small or that is unrepresentative, being constituted by tissue fragments or containing large but normal portal tracts or wide capsule parts, should not be used. Semi-quantitative scoring systems may be preferred for the diagnostic evaluation of fragmented liver biopsies, such as those generally obtained by transjugular puncture. On the other hand, the automatic biopsy devices now available for transjugular punctures yield high-quality hepatic tissue samples that are adequate for image analysis

(26). When sample specimens are adequate, the method

described herein gives accurate fibrosis quantifications through continuous parameters, thus allowing a pre- cise analysis of the variation of liver fibrosis and an accurate prognosis of the functional evolution of the liver. Furthermore, the MPF, MPPF, MPA and MLA quantifications by FibroQuant provide objective and accurate parameters of utility in the evaluation of in viva experimental models and in the clinical follow-up of patients with liver fibrosis.

In conclusion, the present paper illustrates and dis- cusses a novel automated method based on the original digital image processing application FibroQuant. It produces robust, fully reproducible, objective, accurate, reliable and sensitive quantifications of liver fibrosis, which correlate well with Knodell and Scheuer indexes. Thus, our method represents an essential complement to semi-quantitative indexes of fibrosis for better de- fining the evolution stage of different hepatic diseases, fundamental for the accuracy of clinicopathologic di- agnosis, prognosis and follow-up in patients with fi- brosis-inducing liver diseases.

Acknowledgements This work was supported by grant no. SB96- A03887425, Estancias Temporales de Cientificos y Tec- nblogos Extranjeros en Espafia, from the Comision In- terministerial de Ciencia y Tecnologia (CICYT), Spain.

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We thank Ms. F. SLez, Ms. M.D. Rodriguez, Ms. M.J. De Federico, and Ms. F! Alfaro for technical as- sistance in tissue section preparation and staining, and Mr. Richard Davies for revising the English style of the original manuscript.

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