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Improved assessment of multiple sclerosis lesion segmentation agreement via detection and outline error estimates
© Wack et al.; licensee BioMed Central Ltd. 2012
- Received: 8 November 2011
- Accepted: 19 July 2012
- Published: 19 July 2012
Presented is the method “Detection and Outline Error Estimates” (DOEE) for assessing rater agreement in the delineation of multiple sclerosis (MS) lesions. The DOEE method divides operator or rater assessment into two parts: 1) Detection Error (DE) -- rater agreement in detecting the same regions to mark, and 2) Outline Error (OE) -- agreement of the raters in outlining of the same lesion.
DE, OE and Similarity Index (SI) values were calculated for two raters tested on a set of 17 fluid-attenuated inversion-recovery (FLAIR) images of patients with MS. DE, OE, and SI values were tested for dependence with mean total area (MTA) of the raters' Region of Interests (ROIs).
When correlated with MTA, neither DE (ρ = .056, p=.83) nor the ratio of OE to MTA (ρ = .23, p=.37), referred to as Outline Error Rate (OER), exhibited significant correlation. In contrast, SI is found to be strongly correlated with MTA (ρ = .75, p < .001). Furthermore, DE and OER values can be used to model the variation in SI with MTA.
The DE and OER indices are proposed as a better method than SI for comparing rater agreement of ROIs, which also provide specific information for raters to improve their agreement.
- Multiple sclerosis
- Detection and outline error estimates
- Rater agreement
- Operator agreement
- Jaccard Index
- Similarity index
Multiple operators are often used to draw regions of interest (ROIs) on medical images when the workload would be too great for a single operator. When using multiple operators, it is desirable to have the ROIs from each to be similar. There are multiple measures available to assess inter-rater variability, such as Kappa, Jaccard’s Index (JI), Similarity Index (SI), Hausdorff Distances, Conformity and Sensibility, etc. [1–6]. We want to be able to assess an operator’s (or automated method’s) ability to create lesion ROIs, using the ROIs they created. However, for any assessment we should consider whether some test scans are easier or harder than others to achieve good measured agreement on. An ideal measure would solely reflect the operator’s ability, and not the difficulty of the underlying test scans.
One of the original and common results of multiple sclerosis lesion segmentation is the determination of the total lesion volume for an individual subject. A center may validate operators by their ability to draw ROIs that are in agreement with the overall lesion volume of a gold standard analysis. Fortunately, this intra-observer agreement was not found to be significantly correlated with lesion volume . However, this only assesses an operator’s ability to calculate total lesion volume; it does not make a strong statement about the ability of the operator to produce ROIs which agree with a set of ground truth ROIs, since whether small lesions are even marked has little impact. For this purpose, Valmet  or STAPLE (Simultaneous Truth and Performance Level Estimation)  would be a better choice.
SI is perhaps the most commonly used index and is defined as two times the area of the intersection of the raters' ROIs, divided by the sum of the area of the raters ROIs. Unfortunately, the lesion burden (total lesion volume) of MS patients whose scans are used for the comparison of operators' ROIs has been observed to affect an operator's or algorithm's agreement level, with an index such as SI [10–14]. In short, scans depicting high lesion load are “easier” than scans depicting low lesion load for operators to achieve high SI agreement. This is partly because scans depicting high lesion burden will typically have unambiguous large lesions that are hard for raters to disagree in marking. Hence, it is difficult to assess raters or automated methods against one another with SI if they are evaluated on different sets of scans having different lesion burdens. This is the usual case when results are published in the literature, using that center’s data set. Udupa et al. [3, 4] used measures which included an agreement by the raters to mark the same region in their ROIs (detection), and an agreement with respect to how those regions were outlined. Our approach will re-express SI in terms of detection and outline error measurements so that SI's dependency on lesion burden can be well understood.
Our belief is that, unlike SI, our detection and outline error measures won't show a strong dependency on the underlying lesion burden of the scans used in comparing a pair of raters. While scanner issues such as the type of scan acquired and scan quality will still have an impact on operator agreement, reducing the influence of lesion burden (a study population condition) will represent a major improvement for the evaluation of operators. Our development and testing will be performed with operators measuring hyperintense fluid-attenuated inversion-recovery (FLAIR) MRI lesions associated with MS. However, with appropriate testing our method should be directly applicable to ROIs used to measure other hyper- or hypo-intensities, and can also be used to compare a single rater or algorithm to a gold standard. Additionally, we present two graphs for use in comparing a pair of raters. The first compares the raters detection errors (differences) based on region size. The other compares the relative differences between raters in the outline of a lesion.
In Figure 1 (C) we demonstrate the calculation of SI using the original equation and our equation using DE and OE.
The goal of calculating meanOER and meanDE is to provide measures for rater assessment that are better than current indices by virtue of having reduced dependence on MTA.
SI is closely related to Kappa and JI. SI can be shown to be the limit of Kappa  when the agreement between operators includes an increasingly large number of voxels not marked as a lesion by either rater. Furthermore, SI can be expressed in terms of JI as SI = 2 JI / (1 + JI), when JI is defined as equaling the size of the intersection divided by the size of the union of the raters' ROIs. The relationship between SI, JI, and Kappa is nearly linear for the general range we expect in comparing operators’ ROIs, which typically have SI values between .35 and .85.
Cumulative Detection Error: We expect operators to have a greater number of detection disagreements determining whether small hyperintense regions should be marked as lesions, rather than larger regions. We create a Cumulative Detection Error graph to answer the question: "How many ROIs were drawn by only one rater with the ROI sizes greater than a given threshold?" The graph is the number of CR1 and CR2 regions with ROI areas greater than a given area threshold, and decreases with increasing threshold values. In Figure 1 (D), we plot the total number of detection errors for the ROIs in Figure 1 (A). Similarly, we could plot the Cumulative Detection Error graphs for CR1 and CR2 separately. Together, these graphs would indicate if one rater was more lenient or strict than the other rater in determining whether a small hyperintense region should be marked as a lesion.
Outline Error Distribution: We are also concerned with whether one rater consistently creates smaller or larger ROIs than the other. Our approach is to plot the histogram of for all CR12 regions, which we term the Outline Error Distribution graph. Figure 1 (E) shows the Outline Error Distribution graph for the ROIs in Figure 1 (A), which had only 3 CR12 regions. A more typical usage would have hundreds or thousands of CR12s. An ideal distribution would be a thin peak located at 0.
Subjects and MR acquisition
FLAIR scans from 17 participants, aged 18–80 years, with Expanded Disability Status Scores (EDSS)  (0–8.5) fulfilling the criteria for clinically definite MS  were analyzed by two raters. Informed consent was obtained from all participants, and the study was approved by the University at Buffalo's Health Science Institutional Review Board. Scans were performed on a 3 T GE Signa Excite HD 12.0 Twin Speed 8-channel scanner (General Electric, Milwaukee, WI) using a GE multi-channel head and neck coil. FLAIR scans had TE = 120 ms and TR = 8500 ms. Image voxel size was .94×.94×3 mm3. A full description of the scanning protocol was described in a recently published study . Both raters were physicians with several years of MS research experience, but had only three months experience at our lab at the time of the experiment. Lesion contouring was performed with JIM 4.0 software (Xinapse Systems Limited, Aldwincle, U.K.), according to established lab guidelines. Hyperintense lesions were outlined using JIM's semi-automated contouring tool, which allows an operator to fully specify a lesion outline by clicking the mouse only near the edge of the lesion; a few outlines (< 2%) required manual editing to achieve a proper lesion contour. Software was written in-house using MATLAB (Natick, MA).
For each image slice analyzed by both raters, the closed-path line segment JIM ROIs are used to form image masks. The lesion outlines are converted to an image mask by up-sampling the image by a factor of 5 in both the x and y directions to minimize the possibility that a single closed path ROI forms multiple regions, which can occur if an ROI has a narrow section (less than one pixel) between two larger sections. A binary “or” of the two masks is used to form the union. Each distinct separate region of the union “or” operation is referred to as a “Connected Region” (CR). The connected regions are labeled as CR1, CR2, or CR12, based on whether the CR was form by the ROIs of rater 1, rater 2, or raters 1 and 2.
DE, OE, OER, and SI were compared to MTA both graphically and by Spearman rank correlation. The mean values of OER and DE were used to express SI as a function of MTA, and compared to the true values of SI both graphically and using Pearson linear correlation. We also compare our fit of SI to linear and quadratic fits of SI as a function of MTA. Finally, we demonstrate the utility of the Outline Distribution Error and Cumulative Detection Error graphs.
Our expression for SI in terms of OER and DE provided a better fit of the measured SI values across varying lesion loads, both in an absolute and relative sense (i.e., accounting for the number of parameters used), than using the mean of the SI or a linear or quadratic fit of SI values. The sum of the square of the residual errors when fitting the measured SI values by MTA is: .383, .254, .194, and .117; for the models: mean SI value, linear fit, quadratic fit, and our DOEE method, respectively. Furthermore, the respective Akaike Information Criterion values  with correction for finite sample size (AICc) are: -62.19, -66.6, -68.19, and −79.78, respectively. AICc values are relative to each other and account for a varying number of parameters in competing models. The lowest AICc value indicates the model that is most likely the best model from an information theoretic perspective. Hence, the parameters OER and DE provide the best fit of SI’s dependence on lesion load, even accounting for a differing number of parameters for each model. The AICc values also allow us to calculate the likelihood one model is better than another. The likelihood that a mean, linear, or quadratic fit is better than our DOEE method is p < < .0001.
We noted previously that there was not a significant correlation between MTA and OER. Furthermore, when broken down to individual ROI sizes, there was no correlation between lesion size (measured as the union of the two raters) and the intersection/union fraction of the 1131 regions of type CR12 (r = .008, p = .7883). This is an indication that rater outline agreement (as a fraction) is similar for lesions of all sizes.
Our results confirm the observation of others that the agreement level between operators marking hyperintense MS lesions as measured by SI is dependent on the lesion burden shown in the test scans. Using AICc, which accounts for the number of parameters a model uses, the DOEE method was significantly better than the mean value, linear, or quadratic fit of the SI values (p < <.0001, for all three comparisons). Using the mean DE and OER values calculated across scans, our expression for SI in terms of MTA, , has a remarkable .83 linear correlation (p < .001) with the SI values calculated for each pair of ROIs associated with a scan. Likewise, the small residual errors indicate a very good fit (Figure 4). Just as importantly, the values for DE and OER are not significantly correlated with MTA. Hence, it is easy to see that if SI measurement is used then operator agreement will appear poorest when using low lesion load scans (i.e., MTA is small, and DE/MTA is relatively large, in the equation for SI directly above), and best when using high lesion load scans (i.e. high MTA) and SI can be approximated as 1 – (1/2) OER. To summarize, while SI is commonly used to measure rater ability, it heavily reflects the lesion burden of the test set used. However, we are able to explain SI's dependence on lesion burden using just two parameters that are not dependent on lesion burden. We therefore propose these values (meanDE and meanOER) either as an addition or alternative to the reporting of mean SI values for assessing rater agreement.
The shape of the SI values plotted against MTA values (Figure 4) follows an initial steep rise followed by a leveling of values for larger values of MTA. This general shape can be observed in graphs relating SI and lesion burden from other centers . The rank correlation between SI and MTA was highly significant (ρ = .75, p < .001). As values for SI are highly correlated with Kappa and JI, these later indices would also be highly dependent on the lesion burden of patients used in the test set.
Our approach divides operator differences into two types: DE and OE. These two types of errors have different characteristics. DE was predominantly constant for all scans, and had a non-significant (ρ = .056, p = .83) rank correlation with MTA. On the other hand, OE showed a strong linear relationship with MTA (Figure 3). This led to our use of OER in our equation for SI, which has a low rank correlation with MTA (ρ = .23, p = .37). OE's direct dependence on MTA is reasonable. MTA increases when there are more lesions, or the average lesion size increases. In either condition, we expect the outline error to increase. It may seem reasonable to assume a similar relationship with DE. That is, that more lesions imply operators would have a larger absolute number of differences in detecting lesions. However, this is not the case. The predominant relationship is that DE is relatively constant across scans and MTA values (Figure 3) and is well represented by a line with an intercept equal to DE and a slope equal to zero. This relationship suggests operators may have an advantage in agreeing to mark a small lesion (lower rate of detection error) on a scan depicting high lesion burden than a low lesion burden. That is, even though raters must mark more lesions on scans depicting high lesion volume, they will likely have the same total difference in the detection of lesions (DE) as from a scan depicting low lesion burden. We believe that DE remaining relatively constant across a range of lesion loads indicates that total size of “subtle” or ambiguous lesions remains relatively constant across scans. Outline error, on the other hand, can be well represented by a line with an intercept equal to zero, and slope equal to OER (Figure 3).
Detection error measurements, the total size (DE) and number of missed ROIs (Cumulative Detection Error graph), are especially important in the analysis of longitudinal studies. For example, a result of many ROI analyses is to establish the number of (typically small) lesions that may have newly appeared or disappeared with respect to a previous scan. In this regard, agreement measures such as SI, JI, or Kappa—or worse, operator agreement in measuring total lesion volume—are poorly suited to the task. This is especially true if the scans have a high lesion burden, since these measures are fully dominated by the raters' agreement on the outlines of large lesions. If the analysis requires the determination of small lesions, we recommend the use of the Cumulative Detection Error graph to estimate the expected number of detection errors above a given threshold size. We then recommend that a lesion threshold value be chosen for the analysis so the average number of disagreements is small.
OE is the major contributor of error by volume. While for low lesion burden the contributions of OE and DE were similar, OE was more than 5 times larger than DE for scans showing high lesion burden. As such, reducing OE (or OER) should have the greater impact on improving inter-rater measure of lesion volume. It is, therefore, not surprising that outlining of lesions using semi-automated contouring methods has been shown to reduce inter-rater variability compared to manual outlining . The test for correlation between individual CR union and intersection/union was performed for 1131 CR and near zero correlation (r = .008, p = .7883) was observed. This indicates the outline agreement behaves similarly for ROIs of all sizes. The presented Outline Error Distribution graph makes use of this fact and uses ROIs of all sizes for the distribution. Even with the above findings, it is still possible that lesions with similar size will have slightly different values for the intersection/union fraction depending on the overall lesion load of the scan the lesion was from.
Breaking operator agreement into DE and OER allows an operator to be evaluated on either or both criteria according to the demands of the application. Our tests and observations provide an introduction to our developed tools for the comparison of raters creating ROIs of MS lesions. The development was driven from testing automated lesion detection methods. In this work, it quickly became apparent that the success of a method as measured by SI had little to do with the method, but instead was extensively driven by the lesion burden revealed by the images. Automated lesion detection methods are regularly reported in the literature, with their performance typically described in terms of JI, SI, or Kappa. Based on the results presented here, we see that it is difficult for the reader to compare results of different methods, since the lesion burden of the patients used to construct a test set of scans dominates how well a method performs in terms of SI. Had methods similar to ours been used, it would be relatively easy to assess the strengths and weaknesses of the different methods.
OE, DE, and SI only measure the difference between the raters, and don't distinguish between raters or a gold standard with "False Positive" and "False Negative" distinctions . However, our "Cumulative Detection Error" and "Outline Error Distribution" graphs provide an informative approach—which examines whether biases exist between raters—that is consistent with our division between detection and outline differences. The initial observations made here lead to many new questions and research areas. For instance, would the incorporation of lesion contrast either with or in place of lesion size provide a better variable for the functions measuring detection and outline agreement? Additionally, our approach demonstrated usefulness for comparing rater agreement across scanning modalities, which allows us to answer questions such as: “Do raters agree better when measuring ROIs on a 3 T scanner versus a 1.5 T scanner?” Used in this way our method would be able to determine whether a hypothesized improvement is due to improved detection or outline agreement.
While we propose DE and OER as better measures for the comparison of raters' masks than using SI, JI, or Kappa, these still do not strictly measure rater performance alone. In the case of comparing 1.5 T vs. 3 T scanning modalities, this can be used as an advantage. In general, we (obviously) anticipate that raters will perform better on high quality images than on low quality images. However, our methods remove a significant confounding problem in the comparison of raters that afflicts the indices, SI, JI and Kappa. Our testing used ROI sets from two raters on 17 scans, which is more than would typically be used to evaluate a rater, and was sufficient to demonstrate the very strong correlation (r = .83, p < .001) between our estimate and true SI values. The full utility of our measures, as with SI or others, will have to be established over time, as they are used on a wider variety of applications.
Like others, we have shown that SI is dependent on the lesion burden of the patients used in the test sets. However, we have provided an equation for SI's relationship to MTA, based on the calculation of the mean detection and outline errors, which did not have a significant correlation with MTA. We recommend the adoption of detection and outline error methods for the assessment of rater ROIs. Additionally, we've shown that Cumulative Detection Error and Outline Error Distribution graphs provide a center with information on where to focus efforts to improve inter-operator agreement. Based on these advantages, we argue for the use of our measurements for inter-rater agreement assessments of lesion ROIs drawn on FLAIR MRI to improve the quality of ROIs created at a center. The result is an increase in the trust of the subsequent analysis for both studies that rely on measures of total LV, and studies which are focused on individual lesion changes over time.
The authors wish to thank Turi Dalaker, M.D. for her helpful comments; and Heather Yanofsky and Eve Salczynski for their insights, and technical assistance in the preparation of this manuscript.
- Hunttenlocher D, Klanderman G, Ruckelidge W: Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell. 1993, 15 (9): 850-863. 10.1109/34.232073.View ArticleGoogle Scholar
- Liu C, Frazier P, Kumar L: Comparative assessment of the measures of thematic classification accuracy. Remote Sens Environ. 2007, 107 (4): 606-616. 10.1016/j.rse.2006.10.010.View ArticleGoogle Scholar
- Udupa J, Leblanc V, Schmidt H, Imielinska C, Saha K, Grevera G, Zhuge Y, Molholt P, Currie L, Jin Y: A methodology for evaluating image segmentation algorithms. In SPIE Medical Imaging. 2002, 4684: 266-277.Google Scholar
- Udupa J, LeBlanc V, Zhuge Y, Imielinska C, Schmidt H, Currie L, Hirsch B, Woodburn J: A framework for evaluating image segmentation algorithms. Comput Med Imaging Graph. 2006, 30 (2): 75-87. 10.1016/j.compmedimag.2005.12.001.View ArticlePubMedGoogle Scholar
- Chang HH, Zhuang AH, Valentino DJ, Chu WC: Performance measure characterization for evaluating neuroimage segmentation algorithms. Neuroimage. 2009, 47 (1): 122-135. 10.1016/j.neuroimage.2009.03.068.View ArticlePubMedGoogle Scholar
- Zijdenbos A, Dawant B, Margolin R, Palmer A: Morphometric analysis of white matter lesions in mr images: methodand validation. IEEE Trans Med Imaging. 1994, 13 (4): 716-724. 10.1109/42.363096.View ArticlePubMedGoogle Scholar
- Filippi M, Horsfield M, Bressi S, Martinelli V, Baratti C, Reganati P, Campi A, Miller D, Comi G: Intra-and inter-observer agreement of brain MRI lesion volume measurements in multiple sclerosis: a comparison of techniques. Brain. 1995, 118 (6): 1593-1600. 10.1093/brain/118.6.1593.View ArticlePubMedGoogle Scholar
- Gerig G, Jomier M, Chakos M: Valmet: A new validation tool for assessing and improving 3D object segmentation. Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention: 2001. 2001, Springer-Verlag London, UK, 516-523.Google Scholar
- Warfield S, Zou K, Wells W: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging. 2004, 23 (7): 903-921. 10.1109/TMI.2004.828354.View ArticlePubMedPubMed CentralGoogle Scholar
- Adams H, Wagner S, Sobel D, Slivka L, Sipe J, Romine J, Beutler E, Koziol J: Hypointense and hyperintense lesions on magnetic resonance imaging in secondary-progressive MS patients. Eur Neurol. 1999, 42: 52-63. 10.1159/000008069.View ArticlePubMedGoogle Scholar
- Johnston B, Atkins M, Mackiewich B, Anderson M: Segmentation of multiple sclerosis lesions in intensity correctedmultispectral MRI. IEEE Trans Med Imaging. 1996, 15 (2): 154-169. 10.1109/42.491417.View ArticlePubMedGoogle Scholar
- Udupa J, Wei L, Samarasekera S, Miki Y, Van Buchem M, Grossman R: Multiple sclerosis lesion quantification using fuzzy-connectednessprinciples. IEEE Trans Med Imaging. 1997, 16 (5): 598-609. 10.1109/42.640750.View ArticlePubMedGoogle Scholar
- Admiraal-Behloul F, Van Den Heuvel D, Olofsen H, van Osch M, van der Grond J, Van Buchem M, Reiber J: Fully automatic segmentation of white matter hyperintensities in MR images of the elderly. Neuroimage. 2005, 28 (3): 607-617. 10.1016/j.neuroimage.2005.06.061.View ArticlePubMedGoogle Scholar
- Dyrby T, Rostrup E, Baaré W, van Straaten E, Barkhof F, Vrenken H, Ropele S, Schmidt R, Erkinjuntti T, Wahlund L: Segmentation of age-related white matter changes in a clinical multi-center study. Neuroimage. 2008, 41 (2): 335-345. 10.1016/j.neuroimage.2008.02.024.View ArticlePubMedGoogle Scholar
- Kurtzke J: Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983, 33 (11): 1444-1452. 10.1212/WNL.33.11.1444.View ArticlePubMedGoogle Scholar
- Polman C, Reingold S, Edan G, Filippi M, Hartung H, Kappos L, Lublin F, Metz L, McFarland H, O'Connor P: Diagnostic criteria for multiple sclerosis: 2005 revisions to the" McDonald Criteria". Ann Neurol. 2005, 58 (6): 840-846. 10.1002/ana.20703.View ArticlePubMedGoogle Scholar
- Di Perri C, Dwyer M, Wack D, Cox J, Hashmi K, Saluste E, Hussein S, Schirda C, Stosic M, Durfee J: Signal abnormalities on 1.5 and 3 Tesla brain MRI in multiple sclerosis patients and healthy controls. A morphological and spatial quantitative comparison study. Neuroimage. 2009, 47 (4): 1352-1362. 10.1016/j.neuroimage.2009.04.019.View ArticlePubMedGoogle Scholar
- Akaike H: A new look at the statistical model identification. IEEE Trans Automatic Control. 1974, 19 (6): 716-723. 10.1109/TAC.1974.1100705.View ArticleGoogle Scholar
- Anbeek P, Vincken K, van Osch M, Bisschops R, van der Grond J: Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage. 2004, 21 (3): 1037-1044. 10.1016/j.neuroimage.2003.10.012.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2342/12/17/prepub
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