semi-automated segmentation to assess the lateral meniscus ... · kcollege of medicine, division of...

Osteoarthritis and Cartilage (2010) 18, 344e353

ª 2009 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.doi:10.1016/j.joca.2009.10.004

Semi-automated segmentation to assess the lateral meniscus in normaland osteoarthritic kneesM. S. Swansony, J. W. Prescottzy, T. M. Bestx, K. Powellz, R. D. Jacksonk, F. Haqx and M. N. Gurcanz*yCollege of Medicine, The Ohio State University, Columbus, OH, USAzDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, USAxDivision of Sports Medicine, Department of Family Medicine, The Ohio State University Sports Medicine Center,Columbus, OH, USAkCollege of Medicine, Division of Endocrinology, Diabetes and Metabolism, The Ohio State University,Columbus, OH, USA

Summary

Objective: The goal of this study was to develop an algorithm to semi-automatically segment the meniscus in a series of magnetic resonance(MR) images to use for normal knees and those with moderate osteoarthritis (OA).

Method: The segmentation method was developed then evaluated on 10 baseline MR images obtained from subjects with no evidence, symp-toms, or risk factors of knee (OA), and 14 from subjects with established knee OA enrolled in the Osteoarthritis Initiative (OAI). After manuallychoosing a seed point within the meniscus, a threshold level was calculated through a Gaussian fit model. Under anatomical, intensity, andrange constraints, a threshold operation was completed followed by conditional dilation and post-processing. The post-processing operationreevaluates the pixels included and excluded in the area surrounding the meniscus to improve accuracy. The developed method was eval-uated for both normal and degenerative menisci by comparing the segmentation algorithm results with manual segmentations from five humanreaders.

Results: The semi-automated segmentation method produces results similar to those of trained observers, with an average similarity indexover 0.80 for normal participants and 0.75, 0.67, and 0.64 for participants with established knee OA with Osteoarthritis Research SocietyInternational (OARSI) joint space narrowing (JSN) scores of 0, one, and two respectively.

Conclusion: The semi-automatic segmentation method produced accurate and consistent segmentations of the meniscus when compared tomanual segmentations in the assessment of normal menisci in mild to moderate OA. Future studies will examine the change in volume, thick-ness, and intensity characteristics at different stages of OA.ª 2009 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Key words: Meniscus, Segmentation, Osteoarthritis, Magnetic resonance imaging.

Introduction

Osteoarthritis (OA) is the most common form of arthritis andapproximately 40% of persons age 55 and older have fre-quent knee pain or radiographic evidence of knee OA1e3.OA of the knee involves progressive degeneration of boththe bone and soft tissues, including joint space narrowing(JSN), sclerosis, and osteophyte formation. Up to 6% of pa-tients have at least a one point increase in KellgreneLa-wrence (KeL) grade annually, but it is difficult to predictwhich patients will progress and at what rate4. Thus, thereis a clear need to delineate the factors that put patients atthe highest risk for progression to identify targets that maybe amenable for intervention.

The health of the articular cartilage and its effects on theextent of knee OA has received some attention5, however,the meniscus may also modify the development and

*Address correspondence and reprint requests to: M. Gurcan,Department of Biomedical informatics, The Ohio State University,333 W Tenth Avenue, Columbus, OH 43210, United States. Tel:1-614-292-1084; E-mail: [email protected]

Received 3 April 2009; revision accepted 9 October 2009.

344

progression of OA. The meniscus reduces stress on thecartilage in the tibio-femoral compartment by absorbingand distributing force under increasing loads, lubricatingthe joint, and contributing to joint stability6e9. During stand-ing alone, the meniscus distributes 30e55% of the totalbody weight across the articular cartilage. Degeneration ofthe meniscus decreases tibio-femoral contact area by50e70% and accelerates cartilage degradation leading tojoint degeneration6,7,10e19. Although the widely accepteddefinitions of OA do not directly address the health of themeniscus4, the increased incidence of structural joint de-generation after partial and total meniscectomy16 raisesquestions about the effects of this structure in the develop-ment and progression of OA.

Magnetic resonance imaging (MRI) is ideal for quantitativeimage analysis of the meniscus as it permits detailed viewingof the tissue not accessible to radiographs6,20,21. Detection ofmeniscal injury by MRI has an accuracy of up to 85% com-pared to arthroscopic and clinical examination, thereby sup-porting its utility in the diagnostic and treatment algorithm ofthe acutely injured knee20,22,23. Segmenting the meniscusfrom MR images could be the foundation of the analysis for

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345Osteoarthritis and Cartilage Vol. 18, No. 3

changes associated with this tissue and its potential role inOA. Segmentation of the meniscus will permit quantitativemeasures such as volume, mean intensity, intensity distribu-tion, texture, and thickness. Any number of quantitative mea-sures can be compared with OA progression with the intent tofind more reliable biomarkers or yield insight into the naturalhistory of this disease.

Manual segmentation is time consuming, making mean-ingful analysis on large data sets both labor and time inten-sive. In addition, manual segmentations are prone to intraand inter-reader variability, both of which can be largely over-come with computer-assisted segmentation. The purpose ofthis study was to develop a semi-automated segmentationtechnique to characterize the meniscus in order to reducesegmentation time, inter, and intra-reader variability. Thetechnique was designed to allow for flexibility between pa-tients and across a spectrum of joint degenerative states,while maintaining constraints based on known anatomical in-formation. This tool was not designed to completely eliminatethe need for manual segmentation since is cannot performwell on subjects with severe OA or acute meniscal injury,but is rather to be used as a tool to speed up quantitative anal-ysis on large data sets. Indeed, there is a large degree of var-iation among manual readers in such cases, which is whyfurther manual scrutiny is required for reliable analysis.

Methods

IMAGE ACQUISITION

Data used in this study were obtained from the Osteoarthritis Initiative (OAI)database, which is available for public access at http://www.oai.ucsf.edu/.Images from the 0.B.2 release were used with Institutional Review Board

Fig. 1. An example of a Sagittal view of a (a) normal knee, (b) one with OAscore increases, meniscus degeneration becomes appar

approval. The algorithm was designed using Sagittal T2 Map 120 mm field ofview MRI’s obtained from 3 T Siemens machines. The images were384� 384 pixels (pixel spacing¼ 0.3125/0.3125 mm) with a slice thickness of3 mm. The imaging protocol and limb positioning was uniform, with the lateralmeniscus in the right leg being examined in all cases24,25. The right knee waschosen since most work on cartilage quantification available to the OAI hasbeen performed on the right knee. Also, the OAI image database onlyhas data for the right knee available for the image sequence used.

PATIENT SELECTION

The segmentation method was tested on baseline MR images obtained on24 randomly selected participants enrolled in the OAI24; 10 with no evidence ofOA, osteophytes, risk factors, or knee symptoms of pain and stiffness, and 14with evidence of right knee OA defined as frequent knee symptoms in past 12months and KeL score of �2 on fixed flexion radiograph (Progression subco-hort). Participants with established knee OA were further stratified in this studybased on the Osteoarthritis Research Society International (OARSI) JSNscore of 0e3 based on paired X-ray reading of the lateral compartment ofthe right knee (Fig. 1). Four participants were selected at random from eachof JSN¼ 0, JSN¼ 1, JSN¼ 2, and two with JSN¼ 3 totaling 14 patientsfrom the Progression subcohort. Only two participants were selected forJSN¼ 3 because no other participants met this criteria. All participants hadJSN grades of 0 or one for the medial compartment, indicating that diseasewas located mainly in the lateral compartment. All analysis was done on the lat-eral meniscus of the right knee, with future work to examine the medial menis-cus as well. Semi-automatic segmentations of 10 lateral menisci from non-exposed control participants were quantitatively compared with manual seg-mentations, two of which were compared to segmentations of five humanraters for intra and inter-reader variability. The 14 lateral menisci from progres-sion patients were quantitatively examined by five manual raters for inter-reader variability, and three of these were segmented twice by each rater fordetermination of intra-reader variability.

SEGMENTATION METHOD

The semi-automated segmentation consisted of five phases: initialization,threshold determination, segmentation, conditional dilation, and morphologi-cal post-processing26 (Fig. 2).

OARSI 1, (c) OARSI 2, and (d) OARSI 3. Notice that as the OARSIent along with osteophytes and cartilage damage.

http://www.oai.ucsf.edu/

Fig. 2. Flowchart of the segmentation process.

346 M. S. Swanson et al.: Semi-automated meniscus segmentation

Phase I: initialization/manual inputs

The segmentation process starts with two manual inputs: 1) a seed pointwithin the lateral meniscus and 2) the selection of the last slice to be seg-mented. A seed point was chosen by viewing the Sagittal sequence from lat-eral to medial, and choosing a pixel within the lateral meniscus on the first

Fig. 3. Here are three consecutive images showing the lateral meniscus hbe chosen as the end point because the meiscofem

slice that it appeared. In some participants, an image containing merged an-terior and posterior horns was not present, so one seed pixel was selectedfor each horn of the meniscus at their most lateral extent. The final slicethat showed the meniscus was also determined manually, providing the algo-rithm an end point. This endpoint was necessary to manually determine theboundary between the medial extent of the meniscus horns and the menis-cofemoral and cruciate ligaments (Fig. 3). The remaining segmentation stepswere performed automatically.

After determining a seed point, each image underwent a preliminarythresholding operation to identify regions that should be excluded from thesegmentation. First, a mask of pixels with intensities higher than 450 andlower than 120 was generated based on reliable exclusion of non-meniscuspixels determined based on a training set. Then, segmented regions whoseareas were less than five pixels were removed, which allowed this mask toserve as a map of pixels that were not representative of meniscus (Fig. 4).

A second mask was created to identify bone, which was then dilated asan indirect method of roughly excluding many areas of articular cartilagefrom the segmentation (Fig. 5). The tibia mask was dilated with a larger struc-turing element than the femur mask because of the larger separation be-tween the tibia and the tibial cartilage than between the femur and itsdistal cartilage. Additionally, a range filter with a 3� 3 neighborhood wasused as an additional method of edge detection. A mask of pixels whose in-tensity values were greater than 120 was created from the filtered image(Fig. 6). Similar to the intensity and bone constraints, these pixels were ex-cluded from the segmentation because they represent boundaries betweenmeniscus and cartilage.

Since the meniscus maintains a similar size and shape from slice-to-slice,size and shape constraints based on the previous meniscus segment werealso imposed. The meniscus segmentation was prevented from gettingmore than 30% larger than the previous segmentation because the meniscustended to decrease in area from superficial to deep, and was limited toa dilated mask of the previous slice.

Phase II: threshold determination

The algorithm automatically determines a threshold value by interrogatingthe intensity distribution surrounding the seed point within a region of interest(ROI) with a 20� 20 pixel area. Four different regions with Gaussian inten-sity distribution within these areas were observed: 1) dark areas representingmeniscus, 2) high intensity areas within the meniscus, 3) cartilage areas, and4) bone. This task was complicated by the fact that cartilage and high inten-sity areas within the meniscus often had similar intensity values.

Four Gaussian curves were fitted to the histogram of the ROI around theseed point using the least squares method, with each fitted curve represen-tative of a single pixel type (Fig. 7). The fitted Gaussian distributions wereused for determining an adaptive threshold level, allowing for variation be-tween patients and images. Using the fitted histogram, two threshold candi-dates, Threshold1 and Threshold2, were experimentally determined based onthe means (m) and standard deviations (s) of the second and third curves,with the smaller of the two threshold candidates being chosen (Eq. 1, 2).

Threshold1 ¼ m3 � ð3 � s3Þ ð1Þ

Threshold2 ¼ m2 � ð1:25 � s2Þ ð2Þ

Threshold2, was only calculated if the difference between Threshold1 andm2

was less 100, indicating that there was significant overlap of intensity valuesbetween the high intensity meniscus and cartilage curves. Due to natural

orns arranged lateral to medial. In this case, the middle slice wouldoral ligaments predominate in the next image.

Fig. 4. Image (a) is the original image, and (b) is a mask of high and low intensity pixels. This mask was used to determine pixels restrictedfrom being included in the segmentation.


variations in meniscus tissue, a wide range of threshold values may be cal-culated that would lead to inaccurate segmentations. To reduce these ef-fects, the threshold values were further restricted to the range [310e400],which effectively excluded the cartilage regions from the segmentation.

Phase III: initial segmentation

Next, a basic thresholding operation was applied to the slice under con-sideration, where pixels above the calculated threshold were excluded.

Fig. 5. Image (a) shows a mask of high intensity areas. Image (b) isolatemask as an indirect way to exclude it from meniscus seg

The area of thresholding was limited to the area encompassed by a dilationof the previous segmented slice using a 13� 19 rectangular structuring ele-ment. This structuring element limited the segmentation area to only thoseareas where the meniscus was expected. Initial segmentation was followedby a one pixel morphological dilation of the generated mask, filling of holes,and removal of small areas of unconnected pixels. These morphological op-erations were done to connect fragmented areas of the meniscus for use inthe next phase of segmentation (Fig. 8). Notice that the thin layer of menis-cus between the anterior and posterior horns is excluded in Fig. 8(b) due to

s the femur, which is dilated in image (c) to include cartilage in thementation. The same process is done for the tibia.

Fig. 6. Image (a) shows the original image before filtering. Image (b) is the result of a range filter with a 3� 3 neighborhood. Image (c) isa mask of pixels having an intensity value greater than 120 made from the range filtered image. The masked pixels on image (c) are excluded

from segmentation.

Fig. 7. (a) A dilation of a sample segmentation result, (b) with corresponding histogram and fitted Gaussian curves of the region used forthreshold determination.


Fig. 8. Image (a) shows a mask after thresholding and (b) is the mask after morphological operations. Note the high intensity areas within themeniscus (a) which were not included in the thresholding operation. OA participants tended to have a higher proportion of these areas than

those of non-exposed participants.


the morphological operations. This is an inevitable consequence of havinga conservative segmentation approach to reduce the risk of segmentingcartilage.

Phase IV: conditional morphological dilation

The initial threshold segmentation acquired from phase III was used asa starting point for the conditional morphological dilation. The initial seg-mented area was dilated by one pixel with a 3� 3 square structuring ele-ment, and the intensity of each new pixel was tested. The tested pixel wasincluded in the final segmentation if its intensity was below the threshold cal-culated in phase II. After each new pixel was tested, the new segmentedarea was dilated again by one pixel to repeat the cycle, and was repeateduntil no new pixels were added (Fig. 9). After every fourth iteration, the regionwas morphologically closed to fill gaps. The operation was also stopped ifcertain anatomical restrictions were met, namely the cross-sectional area be-coming larger than the previous segment by 30%, the area reaching a pre-set maximum, or the operation exceeding 20 iterations. The pre-set maxi-mum area was determined experimentally based on manually segmented re-gions, and was set at 1100 pixels with an exception for the first and secondslices which was set at 1500 pixels since the meniscus tended to be larger inthose slices.

Fig. 9. The same mask shown in Fig. 7(b) after the growth phasewas completed. The edges of the meniscus are now better defined.

Due to overlap of the Gaussian curves between high intensity valueswithin the meniscus, and high intensity values representative of cartilage,a conservative threshold value was calculated which prevented some menis-cus pixels from being included in the segmentation. This situation most oftenoccurred when the region growing phase ended prematurely because of itssmall size and presence of high intensity areas within the meniscus. To over-come this problem, the calculated threshold was flexible during the condi-tional dilation phase. Using anatomic information gathered from manuallysegmented cases, it was found that the meniscus was never smaller than105 pixels and that the horns of each meniscus rarely reduced in area bymore than 25% from one slice to the next. Using this information, if the seg-mentation was stopped before these criteria were met, the threshold wastemporarily increased by 10% until the bordering pixels were included to con-tinue the region growing phase of the segmentation. This variable thresholdensured that the segmentation was not stopped prematurely, and that highintensity areas within the meniscus were included.

Phase V: post-processing

Two morphological post-processing steps were used to further improvethe segmentation accuracy. First, all pixels that fell within the intensity andrange constraints outlined in phase I were removed from the segmentation.Second, pixels that were not on the previous segmented slice but were in-cluded on the current slice had their intensities tested again with a decreasedthreshold to exclude pixels that were more likely to be included incorrectly.Similarly, pixels that were included on the previous segment but had notbeen included on the current segmentation had their intensities retestedwith a higher threshold to increase the likelihood of acceptance. This secondpart of these post-processing steps was only performed on slices where themeniscus horns were separated since from that point on, the location of themeniscus from slice-to-slice was less variable. The final segmentation wascompleted after one additional growth iteration to include low intensity pixelsthat had not been included before, possibly due to incomplete conditionaldilation or imposed range constraints.

Fig. 10. The green outline is a segment completed by a humanreader, and the yellow outline is the segmentation completed bythe semi-automated algorithm. Note the high degree of similarity(0.91), even when the posterior horn of the meniscus has high

intensity areas throughout.

Table IIntra-Reader Variability

Reader OARSI (OAI subcohort) Mean

0 (Control) 0 (Control) 0 (Progression) 1 (Progression) 2 (Progression)

Semi-automated segmentation .98 1.0 .99 1.0 .99 .99Manual 1 0.84 0.85 0.90 0.88 0.86 0.87Manual 2 0.85 0.88 0.69 0.82 0.71 0.79Manual 3 0.88 0.88 0.86 0.84 0.88 0.87Manual 4 0.83 0.86 0.78 0.84 0.76 0.81Manual 5 0.88 0.83 0.84 0.85 0.81 0.84


After the segmentation was completed for the image, the next image inthe sequence underwent the same segmentation phases, beginning at theconstraint determination imposed in phase I. The histogram used in phaseII for threshold calculation was generated based on a dilation of the com-pleted segment of the previous slice, and a new threshold was calculatedfor the anterior and posterior horns separately. Throughout each phase ofthe segmentation, the anterior and posterior horns were treated separatelydue to variations in pathology and vascularization9,27, allowing for the thresh-old and conditional dilation restrictions to be unique for each horn. An exam-ple of the final meniscus segmentation is shown in Fig. 10, and the ZSI canbe compared to the mean manual intra-reader variability seen in normalknees of 0.86 (Table I), indicating a similar degree of discrepancy betweenmanual raters.

VALIDATION

Semi-automated segmentations were quantitatively compared to those ofmanual raters according to the similarity index described by Zijdenbossimilarity index (ZSI)28,29.

S ¼ 2½A1XA2�½A1 þA2�

ð3Þ

A1 and A2 represented all pixels included in a segmented region, and S wasdefined as twice the area of overlapping pixels divided by the added area ofboth segmented regions. S varies between 0 and one, where one indicatescomplete agreement, and 0 indicates regions that do not overlap. This mea-sure of similarity was dependent on degree of overlap between the two re-gions and location of the two regions, giving a higher score if two regionsshared similar centers27. According to Zijdenbos, a similarity index above

Table IIParticipant demographics

Lateral JSNOARSI grade

Cohort Sex Race Age BMI

0 Normal F African American 47 22.30 Normal F Caucasian 53 20.60 Normal F Caucasian 54 20.10 Normal M Asian 55 24.60 Normal M Caucasian 55 27.00 Normal M Caucasian 55 28.30 Normal M African American 56 25.70 Normal M Caucasian 57 24.10 Normal F Caucasian 70 21.40 Normal M Caucasian 72 20.80 Progression M African American 46 27.20 Progression M Caucasian 48 25.90 Progression M Caucasian 52 33.60 Progression F 55 27.81 Progression F Caucasian 51 35.91 Progression F African American 51 43.01 Progression F 59 30.91 Progression M Caucasian 62 34.92 Progression M Caucasian 46 24.82 Progression M Caucasian 47 28.82 Progression M 51 26.72 Progression F Caucasian 52 20.93 Progression M Caucasian 48 26.53 Progression M Caucasian 55 27.3

BMI, body mass index.

0.7 provided ‘‘excellent agreement’’, and 0.6 indicated ‘‘substantial agree-ment’’ between two regions29.

In order to quantitatively measure the accuracy of the semi-automatedsegmentation results, five readers were trained to manually segment the me-niscus using ‘‘Image-J’’, an image viewing and segmenting program fromThe National Institutes of Health (NIH). Each reader outlined the lateral me-niscus of two healthy and twelve progression OA cases of varying severity.These segmentations were then compared to those of each other rater usingthe ZSI in Eq. 3. Each of five raters’ segmentations had ZSI calculated incomparison to the rest of the raters and the semi-automated segmentations.Due to varying interpretations between raters about the lateral and medialextent of the meniscus, only slices where all raters agreed that meniscuswas present were compared. The intent of this study was to determine thevalidity and viability of semi-automated segmentations, so the analysis wasfocused on the performance compared to manual raters. Slices where man-ual raters disagreed would factor in a ZSI of 0, which would inappropriatelyskew the comparison results.

Results

Images from 10 normal knees and 14 knees with OAwere analyzed in this study. The average age of partici-pants was 54 years; approximately 38% were female and80% were white (Table II).

Similarity data of the manual and automatic segmenta-tions of the lateral meniscus showed that the semi-auto-mated segmentation method performs similarly to manualraters for normal and moderately degenerated menisci(Table III). Rater one marked eight additional non-exposedcontrol cases, all with a ZSI that yielded excellent correla-tion (similarity index> 0.70) with manual raters. A visual ex-ample of average results is seen in Fig. 11. All five manualraters were unable to identify a meniscus in OARSI grade 3participants, therefore these images were excluded.

The automatic segmentation had an average similarity in-dex 0.80 when used on control participants, and had an av-erage similarity of 0.75, 0.67, and 0.64 when looking atprogression patients with OARSI JSN of 0, one, and two, re-spectively. The algorithm, including manual input, was ableto produce segmentation results in 2e4 min per case for

Table IIIOverall performance of semi-automatic segmentations on non-ex-posed and OA progression cases compared to a manual rater.No manual rater was able to identify meniscus in the OARSI 3

participants

Mean similarityindex (Standard

deviation)

Standarddeviation

Non-exposed control (n¼ 10) .80 .06Progression subcohort (n¼ 12) .69 .12OARSI JSN grade¼ 0 (n¼ 4) .75 .04OARSI JSN grade¼ 1 (n¼ 4) .67 .12OARSI JSN grade¼ 2 (n¼ 4) .64 .16OARSI JSN grade¼ 3 (n¼ 2) N/A N/A

Fig. 11. This is an automatic segmentation (yellow) with a similarityindex of .79 compared to a manual rater (green) showing the aver-

age level of agreement for normal knees (see Table III).


both normal and progression subjects, while manual seg-mentations consistently took 7e20 min per case, dependingon the speed of the reader and the familiarity with the seg-mentation program. The running time for the software wasnot optimized for speed, which can bring further reductionsin time without sacrificing accuracy.

Intra-reader variability was assessed by examining thesegmentations of each manual rater to previous segmenta-tions of the same images from the same rater. The semi-au-tomated method was assessed for intra-reader variability bychoosing 100 random seed points within the meniscus, andcomparing each segmentation result to the previous result.Semi-automated intra-reader segmentations yielded an av-erage ZSI of 0.99, while the manual segmentations averag-ing 0.84 (Table I).

Areas with low cartilage intensity posed a challenge forthe segmentation method and reduced the accuracy inthese instances (Fig. 12). An example of an entire lateralmeniscus series is shown in Fig. 13, with correspondingdata on how the segments improved based on comparisonswith and without phase V post segmentation processingcompared to a single manual rater (Table IV). The averageimprovement was 1.1% per segmentation, with an overallimprovement of 0.94%.

Discussion

A more complete understanding of important questionssuch as natural history and risk factors for OA should resultfrom additional knowledge about the meniscus and its role

Fig. 12. This is a more extreme example of a segmentation errorwhere incorrect areas were segmented due to a low intensityarea in the cartilage. The yellow outline is the automatic segmenta-tion, and the green outline is a segmentation performed manually.The error is limited due to area and intensity constraints, and has

a similarity index of 0.66.

in this disease. A study performed by Link et al. comparedfindings on medical images to KeL score, and noted thatmeniscal lesions were present in 46 out of 50 OA patients,and that four patients without lesions had low KeL scores(one or two). Knees with a KeL score of four had severemeniscal lesions characterized as complex tears with defor-mity or severe destruction22. In addition, the tibio-femoralcontact area is correlated with the severity of OA, and themeniscus plays a large role in joint mechanics30. Thus, itis inferred that the health of the meniscus may play a rolein the progression of OA, however definitive studies ad-dressing this important issue are limited. Quantifying thechanges of the meniscus as OA progresses (as seen inFig. 1) has the potential to establish disease severity andpredict disease trend. Herein, we report on a semi-auto-mated segmentation method that demonstrated acceptableaccuracy, intra-reader variability, and efficiency in charac-terizing the meniscus in healthy and moderately osteoar-thritic knees, which should prove helpful in addressingthese and other important questions from longitudinal stud-ies such as the OAI (Fig. 13). Although the sample size islimited, this study determined that meniscus segmentationis feasible and indeed can be very accurate.

The usefulness of computer-assisted segmentationmethods can be appreciated when examining their use in car-tilage segmentation. Several semi-automated and automatedmethods have been developed to enhance quantitative analy-sis of the knee articular cartilage because of their speed, reli-ability, accuracy, and decreased inter-reader variability31.One study reported that automated segmentation results in im-proved inter-reader variability compared with two manualreaders when measuring number of segmented pixels, maxi-mal thickness, and volume31. These improvements over man-ual segmentations offer promise for enhanced objectivemeasurement of natural history, osteoarthritic cartilagechanges, and the effects of therapeutic interventions.

The same advantages achieved by computer-assistedsegmentation of the cartilage were addressed for the me-niscus in the current study. Our semi-automated approachovercomes many segmentation challenges through theuse of carefully designed image intensity and anatomicalconstraints, shown by its consistently high similarity indexfor normal and pathologic knees compared against fivemanual raters. Therefore, our method shows particularpromise with assessment of normal menisci and thosewith mild to moderate OA, making it ideal for use in assess-ing OA development and progression. The use of a singlesemi-automated approach to a large dataset should greatlyreduce effects of intra-reader variability, and eliminate inter-reader variability compared to manual segmentations. AsTable I indicates, manual segmentations are subject to in-evitable intra-reader variation, a limitation which is largelyovercome with semi-automated segmentation. An importantadvantage of a semi-automated segmentation method isthat the determination of borders is unbiased and consis-tent. Through observation of manual segmentations in thecurrent study, it was noted that a manual rater can have sig-nificantly different determinations of a meniscus borderwhen given the same segmentation case at a differenttime. This intra-reader variability is virtually eliminated withour semi-automated segmentation method, as demon-strated by the average similarity index score of 0.99(Table I). The key to reliable image data analysis is a fast,consistent, and reliable method of segmentation, which isbest achieved through automated segmentation methods.The degree of similarity and the speed offered by this seg-mentation method allows for rapid detection and

Fig. 13. A series showing the manual segmentations (green) and automatic segmentations (yellow) of the lateral meniscus from a patient withOA progression.


quantification of meniscus properties without the burden ofexcessive manual time and intra-reader variability, whichshould provide the ability to further our understanding ofthe meniscus and its role in OA.

Semi-automated segmentation is not without its limita-tions. Our algorithm does not perform well in patients withsevere OA or and likely in those with acute meniscal tears.This method is best used as a tool to facilitate analysis ofthe meniscus in large studies since it performs well inmost cases and works faster and without intra-reader vari-ability when compared to manual segmentation. We recom-mend that in cases where it does not perform well thatmanually segmentation by experienced raters be employedsince these cases are often prone to the greatest degree ofmanual inter-rater variability.

One way to enhance our understanding of the relation-ship between meniscus degradation and OA is to examinethe tissue’s volume change as OA progresses. Recently,the volume of the meniscus has been calculated usingcross-sectional area from each slice in the MR image se-quence and the distance between slices32, an approachthat can be easily applied using segmentations. The auto-matic segmentations produced in the present study demon-strated a cross-sectional area similar to that of manualraters, suggesting the potential of this approach in calculat-ing tissue volumes. The automated segmentations consis-tently produced a high degree of similarity to manualsegmentations, making our method suitable for quantitative

Table IVSimilarity index improvement due to phase V

Overall similarity indeximprovement

Case 1 (OARSI 0) �0.01Case 2 (OARSI 0) 0.01Case 4 (OARSI 0) 0.01Case 5 (OARSI 1) 0.02Case 3 (OARSI 2) 0.02Average 0.01

analysis of the meniscus. As the algorithm improves to in-corporate more complex menisci and more degeneratedknees, it may be necessary to employ the segmentation ex-pertise of musculoskeletal radiologists for the evaluation ofaccuracy. A focus of our group is the implementation ofthese methods to the OAI dataset to determine the role ofthe meniscus as an indicator of OA progression or predictorof disease course, making it a potential biomarker for dis-ease progression. Future efforts will expand the algorithmto include the medial meniscus in order to provide a morecomprehensive examination of the knee. Given the morecomplicated structure of the lateral meniscus, segmentationshould also be feasible for the medial meniscus using sim-ilar methodology.

Conflict of interest

The authors have no conflicts of interest to report.

Acknowledgements

This project was supported in part by Award NumberUL1RR025755 and TL1RR025753 (MSS) from the NationalCenter For Research Resources, the NIH Roadmap Train-ing Program in Clinical Research, and the Award NumberR01LM010119 from the National Library of Medicine. Thecontent is solely the responsibility of the authors and doesnot necessarily represent the official views of the NationalLibrary of Medicine, or the National Institutes of Health.The OAI is a public-private partnership comprised of fivecontracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the Na-tional Institutes of Health, a branch of the Department ofHealth and Human Services, and conducted by the OAIStudy Investigators. Private funding partners include MerckResearch Laboratories; Novartis Pharmaceuticals Corpora-tion, GlaxoSmithKline; and Pfizer, Inc. Private sector fund-ing for the OAI is managed by the Foundation for the


National Institutes of Health. This manuscript has receivedthe approval of the OAI Publications Committee based ona review of its scientific content and data interpretation.

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