Evaluation of Cardiac Biventricular SegmentationFrom Multiaxis MRI Data: A Multicenter Study
Jyrki M.P. Lotjonen, DSc,1* Vesa M. Jarvinen, MD,2 Benjamin Cheong, MD,3
Edwin Wu, MD,4 Sari Kivisto, MD,5 Juha R. Koikkalainen, DSc,1 Jussi J.O. Mattila, MSc,1
Helena M. Kervinen, MD,6 Raja Muthupillai, PhD,3 Florence H. Sheehan, MD,7 andKirsi Lauerma, MD5
Purpose: To validate a volumetric biventricular segmenta-tion solution for multiaxis cardiac magnetic resonance(CMR) images.
Materials and Methods: The study population comprised40 subjects. Biventricular end-diastolic and -systolicphases were segmented from both short-axis and horizon-tal long-axis or transaxial cine CMR images. Segmentationwas based on fitting nonrigidly a 3D surface model to mul-tiaxis CMR images. Five segmentations were performed:two manual segmentations by experts, automatic segmen-tation, and two segmentations where a user was allowed tocorrect errors in the automatic segmentation for 2 minutesand without time limits. Volumetry, distance measures,and visual grading were used to evaluate the quality of thesegmentation.
Results: No difference was observed between automaticand manual segmentations in volumetric measures of theventricles. The manual segmentation performed better forleft-ventricular myocardial volume. The distance betweensurfaces as well as visual analysis did not show differencesbetween automatic and manual segmentation for the endo-cardial border of the left ventricle but some corrections areneeded for the right ventricle.
Conclusion: Fully automatic segmentation produces goodresults in the assessment of left ventricular volume anden-docardial border. Two minutes of user interaction areneeded to obtain accurate results for the right ventricle.
CINE CARDIOVASCULAR MAGNETIC RESONANCE(CMR) imaging is an accurate and reproducible tool forassessment of cardiac chamber volumes, ventricularmass, and volumetric function. These measurementsare valuable in diagnostics and follow-up of congenitaland acquired myocardial and valvular diseases.
The ventricular chambers are usually imaged with acontiguous stack of short-axis images from apex tobase. The image analysis is done by manually delineat-ing the left ventricular epicardial and endocardial bor-ders in each section at end-diastole (ED) and end-sys-tole (ES), and by summing up the individual volumes togive the left ventricular muscle volume, and ED and ESventricular volumes. These volumes are used in calcu-lating the stroke volume, ejection fraction, and musclemass. The right ventricular volumes are assessed sim-ilarly. The interanalyst reproducibility of these mea-surements has been 4%–8% for the left ventricle (LV)(1,2) and 6%–17% (3) for the right ventricle (RV). Theintraobserver repeatability has been 3%–6% (1,4) forthe left and 4%–7% (3) for the right ventricular volumes.Usually the true apex is not included in the analysisand the left ventricular base is also sometimes omitteddue to difficulties in the determination of the atrioven-tricular border. By these omissions the speed of analy-sis and the reproducibility improve, but the volumesare underestimated.
The clinical use of volumetric analysis has beenlimited by the analysis time it requires. Even usingonly the ED and ES phases of the left ventricle, seg-mentation may take 30 minutes or more. A relativelyshort segmentation time of 13 minutes was recentlyreported for one commercial software tool (5). How-
1VTT Technical Research Centre of Finland, Tampere, Finland.2Clinical Physiology, Hyvinkaa Hospital, Hyvinkaa, Finland.3MRI Research, Department of Radiology, St. Luke’s Episcopal Hospi-tal, Houston, Texas, USA.4Feinberg Cardiovascular Research Institute, Bluhm CardiovascularInstitute, Northwestern University Feinberg School of Medicine, Chi-cago, Illinois, USA.5Helsinki Medical Imaging Center, Helsinki University Hospital, Hel-sinki, Finland.6Department of Medicine, Hyvinkaa Hospital, Hyvinkaa, Finland.7Cardiovascular Research and Training Center, University of Washing-ton, Seattle, Washington, USA.Contract grant sponsor: Finnish Funding Agency for Technology andInnovation (Tekes) (to VTT).*Address reprint requests to: J.L., VTT Technical Research Centre ofFinland, Tekniikankatu 1, P.O. Box 1300, FI-33101 Tampere, Finland.E-mail: [email protected] November 16, 2007; Accepted June 11, 2008.DOI 10.1002/jmri.21520Published online in Wiley InterScience (www.interscience.wiley.com).
JOURNAL OF MAGNETIC RESONANCE IMAGING 28:626–636 (2008)
ever, there is still a clear need for further reducing theanalysis time. In addition, other structures, such asthe RV and atria, are highly interesting, but laborioussegmentation makes their analysis unattainable inclinical practice.
For a precise analysis the entire chamber volumefrom the ventricular apexes to the inflow and outflowvalves must be included in volumetric analysis. Bycombining long-axis sections with short-axis sectionsin the dataset the ventricular apex and the basal partsof the left and especially the right ventricle can be de-lineated with improved certainty. Despite these clearbenefits the use of multiaxis data has not been widelyreported in CMR segmentation (6–8).
The purpose of this study was to validate fully auto-matic and semiautomatic solutions for the segmenta-tion of the epicardial and endocardial borders of the LVand the endocardial border of the RV using multiaxisCMR images. Our segmentation solution is based onfitting a triangulated surface model simultaneously toall multiaxis data available from a subject, first fullyautomatically, continued by manual editing. The qual-ity of the results was validated against two independentmanual segmentations generated for each case by ex-perts from four hospitals. These segmentations repre-sent a current clinical gold standard but contain somerandom and systematic differences. This means thatautomatically generated segmentation can becomeeven better than our gold standard. For example, invisual analysis an expert may consider that the bordersof automatic segmentation correspond better, in her/his opinion, to the correct segmentation than the man-ual one, made by another expert. In this work the qual-ity of segmentation refers to the agreement with themanual segmentations in three aspects: volumetry, dis-tance between surfaces, and visual goodness. In addi-tion to the quality, the time needed for manual editing isan important factor in considering the utility of a seg-mentation solution. We limited the user-interaction to 2minutes, which we considered to overcome current so-lutions and to be acceptable in clinical practice.
In many studies the quantitative analysis of CMRimages concentrates on the left ventricle only and thestandard short-axis (single-axis) stacks are used. Weshow that only 2 minutes user-interaction is needed toreach clinically satisfactory segmentation quality forthe both ventricles by using multiaxis cine CMR im-ages. Our contribution is a novel approach for segment-ing multiaxis CMR data including a new segmentationalgorithm and tools to interactively modify the automat-ically generated result.
MATERIALS AND METHODS
Study Population and CMR imaging
The Research Ethics Board, Helsinki University Hospi-tal, approved the study and informed written consentwas obtained from the participants.
The study population comprised 40 subjects (20 fe-males; 49 � 16 years) consisting of 15 healthy controls,14 patients with ischemic heart disease, and 11 pa-tients with dilated cardiomyopathy in the early phase ofthe disease. The sample size was limited to 40 cases dueto laborious manual segmentation. The number is wellcomparable with many other CMR segmentation vali-dation studies (6–11). CMR is known to be more repro-ducible and to require a substantially smaller samplesize than echocardiography to show significant differ-ences in cardiac measurements (12).
The ventricular volumes varied to a great degree inour healthy subjects and patients. The ranges of vari-ous clinical measures for the LV and RV, respectively,were 63–336 mL and 72–278 mL for end-diastolic vol-ume (EDV), 14–225 mL and 36–157 mL for end-systolicvolume (ESV), 42–190 mL and 18–150 mL for strokevolume (SV), and 33%–77% and 24%–64% for ejectionfraction (EF). The data were gathered and analyzed ret-rospectively from four different clinical studies (Table1). Breath-hold true FISP cine series were acquired us-ing 1.5 T Siemens Sonata and Siemens Symphony im-agers (Siemens, Erlangen, Germany) with a phased ar-ray coil at two hospitals: Helsinki University Hospitaland Hyvinkaa Hospital. The pixel size varied from1.17 � 1.17 mm to 1.98 � 1.98 mm (1.50 � 0.24 mm).The slice thickness was 5–10 mm (7.1 � 1.6 mm) andthe gap between slices was 0–8 mm (2.6 � 3.0 mm).The number of time phases was 17–50. HeterogeneousCMR data were used to evaluate the performance withvariable clinical data used at different sites and studies.
Semiautomatic Segmentation Solution
The basic idea of our solution is to fit a triangulatedsurface model consisting of endo- and epicardial bor-ders of the LV and endocardial border of the RV tomultiaxis CMR images of a patient (Fig. 1). The model isa mean model of the heart built from 25 healthy volun-teers. Our semiautomatic segmentation solution con-sists of two steps: 1) fully automatic deformation of themean model to image data, and 2) the correction ofsegmentation errors interactively.
Our prototype software solution was implemented inthe Microsoft Windows environment using BorlandC�� Builder. The fully automatic segmentationmethod was implemented in ANSI-C.
Table 1Study Population (N�40)
Controls Patients Disease of study Short-axis Long-axis Transaxial
The localization of the LV cavity from CMR images is anecessary preprocessing step before accurate segmen-tation of the LV borders. This preprocessing step is byitself a nontrivial challenge (13,14). In our approach,the heart is first coarsely located from short-axis im-ages by searching the area where the intensity changesare largest through cine series. This is accomplished bysliding a template, representing the average heart, overthe images and defining the location where the sum ofintensity changes under the template is at a maximum.A subimage is extracted around this location in eachslice. Then the peaks of the blood and myocardium aredetected from a gray-scale histogram and the imagesare normalized by mapping the gray-values corre-sponding to the peaks of the myocardium and blood toconstant values (here 80 and 170, respectively). There-
after, the images are thresholded (here 105) and theventricles are detected based on features such as loca-tion, shape, and size, defined for the binary objects.Based on this information the most basal short-axisslice is defined and the heart is finally extracted alsofrom the images acquired in other orientations.
Correction of Breathing Artifacts
The location of the heart relative to the thorax variesdue to breathing: the heart can move more than 2 cmdue to respiration (15). If 3D segmentation is performedand multiaxis images are used, misalignments betweendifferent cine series must be corrected. Artifacts existbecause cine series have been acquired during differentbreath-holds. We used the registration technique pro-posed in Ref. (16) where the following two steps arerepeated: 1) choose randomly one of the cine series, and2) move it toward the location where similarity betweenthis series and its cross-section between all other cineseries is maximized. In other words, the location of ashort-axis slice is optimized based on long-axis dataand vice versa.
Detection of Landmarks
Next, about 400 landmarks were located in the images.The landmarks defined from long-axis or transaxial im-ages, typically about 20 landmarks, include points fromthe apex of the LV and RV, from their basal free wall andthe base of the ventricular septum. Landmarks fromthe edges of the endo- and epicardial borders of the LVand the endocardial border of the RV were detected inshort-axis images. The distance between neighboringlandmarks was �7 mm. The preprocessing performedin the previous steps provides coarse knowledge aboutthe locations of the ventricles and constrain the searchspace for the landmarks. The detection of accurate lo-cations of the landmarks is based on a high number ofrules developed separately for each region.
Fitting the Mean Model to Landmarks
In the final step the mean model was registered to thelandmarks first using translation, rotation, and scal-ing, and thereafter using local elastic transformations.The elastic transformations were accomplished by thecommonly known free-form deformation (FFD) gridtechnique (17) where a model is deformed based onmovements of an underlying grid. Because the land-mark set is relatively sparse, sparse FFD grids wereused. In addition, the commonly known global-to-localapproach was used, ie, degrees of freedom were gradu-ally increased during the deformation, because the de-formable model-based techniques are known to be sen-sitive to the initialization of the model. We started witha 3 � 3 � 3 grid and increased the size during theiteration up to the 9 � 9 � 9 grid size. The deformationwas formulated as a standard optimization problem,where a weighted sum of two components was mini-mized: the distance of the mean model surfaces fromthe landmarks was minimized while changes in thesurface normal directions of the mean model were si-multaneously regulated.
Figure 1. a: Mean model consisting of surfaces of the endo-and epicardial borders of the left ventricle and the endocardialborder of the right ventricle. The planes indicate approximatelocations of the cross-sections used in b. b: Two short-axis andone long-axis slice from one subject: original images (left col-umn), the mean model superimposed on the images beforemanual segmentation (middle column), and the mean modelsuperimposed on the images after manual segmentation (rightcolumn).
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Interactive Corrections
We have developed a software tool where a 3D surfacemodel is interactively fitted to all multiaxis data avail-able (Fig. 2). The initial model can be either the result ofthe automatic segmentation (semiautomatic mode) orthe original mean model (manual mode). In manualsegmentation the user first fits the model coarsely toimages using translation, rotation, and scaling. Theuser can start directly by modifying the model locally inthe semiautomatic mode.
In Fig. 2 the left-bottom subwindow contains an orig-inal slice from a long-axis stack. The right-top and left-top subwindows contain orthogonal cross-sectionsfrom the stack. The locations of the cross-sections areshown by gray vertical and horizontal lines on thesesubwindows, called spatial subwindows. The blackstripes on the top row are due to the gaps betweenslices. The contours of the objects superimposed onimages are derived from the cross-sections of the sur-face model and the corresponding slice from the stack.The user can switch between stacks in real time and thetool shows the corresponding cross-sections from themodel.
The user deforms the model locally by dragging anddropping the surfaces. The deformation happens in-side a sphere: the user defines movement vector inthe center of the sphere, the software computes aparallel deformation field inside the sphere, and fi-nally all surface points inside the sphere are trans-formed according to the deformation field (Fig. 3). Thewhite sphere shown on the subwindows (Fig. 2)shows the deformation sphere. The user can choosethe location and the size of the sphere freely. Al-though the user drags and drops the contours on 2Dsubimages, it is important to understand that thedeformation itself happens in 3D for surface points.The 3D approach has clear benefits. First, it makesthe segmentation consistent between slices of an im-age stack by keeping the 3D shape realistic andtherefore potentially more accurate. Second, the usercan modify the segmentation in any available imageorientation. In 2D approaches the user may segmentseparately both short-axis and long-axis stacks, lead-ing to two separate set of clinical measures. In ourapproach the 3D surface model is fitted to both direc-tions simultaneously, and only one set of measures is
Figure 2. The user interface of the segmentation solution.
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produced. In practice, the user shifts between imag-ing directions and improves the accuracy gradually.
Although the segmentation is validated only at theED and ES phases in this work, the software tool allowsfull 4D (3D spatial � time) processing. The deformationsphere also has a radius in the time domain, and alltime phases inside the radius from the current timephase are deformed simultaneously. The right-bottomsubwindow in Fig. 2, called the temporal subwindow,links the spatial and time domains. As the user relo-cates the deformation sphere in any spatial subwindowthe software defines the closest point on the surfaceand computes a 3D line passing through the point inthe direction of the surface normal (the black line in theleft-bottom subwindow). A line profile for each timephase of the gray-scale data is derived along the 3Dline. The temporal subwindow is formed by stacking theline profiles. Simultaneously, the cross-sections of thesurface model and the line are computed with all timephases and projected to the subwindow. The black lineindicated in the temporal subwindow shows the 3D linein that domain. The horizontal line around the blackline indicates the radius of the sphere in the time do-main. The user can modify the surfaces also in thetemporal subwindow.
Image Analysis
ED and ES phases were segmented twice by experi-enced physicians producing two independent manualsegmentations per subject (denoted ‘Man A’ and ‘ManB’). The laborious segmentation task was dividedamong four physicians (two cardiologists, one radiolo-gist, and one clinical physiologist) from four hospitalsfrom two countries. Each expert had several years ex-perience in interpreting cardiac CMR images. The num-
ber of segmentations produced by the experts was 32,23, 15, and 10.
Standard 2D, slice-by-slice based tools could not beused in manual segmentation as the fusion of segmen-tations from different imaging orientations is not a triv-ial task. We used our multiaxis segmentation solutionin the manual mode, ie, the user fitted manually themean model to all available images. Experts were askedto make segmentations that they considered correct.Only two constraints were given: 1) papillary musclesand trabeculae were included in the blood volume, and2) if a conflict existed between different imaging direc-tions, eg, because of slice misalignments due to breath-ing or uncertainty in defining borders due to nonopti-mal imaging orientation, long-axis or transaxial datawas preferred in defining the base and apex of bothventricles, and short-axis data elsewhere.
Frequently, trained nurses and technicians performleft-ventricular segmentation in addition to physicians.Therefore, in addition to two independent segmenta-tions made by physicians, two other manual segmen-tations were made by a person with technical educa-tion, experienced in segmenting cardiac CMR images.In these semiautomatic segmentations the interactionwas started from the result of automatic segmentation.In the first of these segmentations, 2 minutes interac-tion time was allowed (denoted by ‘Auto�2min’). Thissegmentation cannot be considered real manual seg-mentation, as some errors may remain due to the timelimit. In the second segmentation the user continuedfrom the 2-minute segmentation and unlimited timewas allowed for making corrections (denoted by‘Auto�Nmin’). This segmentation can be consideredmanual in the sense that the final result was fully un-der the control of the user. However, the result is obvi-ously biased toward automatic segmentation and can-not be used as a reference to evaluate the accuracy ofthe automatic method. The result of the fully automaticsegmentation is denoted by ‘Auto.’
Statistical Inference
The goodness of the segmentations based on the auto-matic results (‘Auto,’ ‘Auto�2min,’ and ‘Auto�Nmin’)was evaluated against two manual segmentations(‘Man A’ and ‘Man B’). Both manual segmentations werecompared separately with a third segmentation (‘Auto,’‘Auto�2min,’ or ‘Auto�Nmin’). Our H0 hypothesis wasthat no difference exists. Statistically significant differ-ence was detected if P � 0.05 for the both manualsegmentations. If the Kolmogorov–Smirnov test indi-cated that distributions did not differ from a normaldistribution, a two-tailed paired t-test was used, other-wise the Wilcoxon rank sum test for paired samples wasused (SPSS 14.0 for Windows, Chicago, IL). Three as-pects of segmentation quality were studied: volumetry,distance-based measures, and visual quality.
Volumetric Analysis
The ability to estimate the volumes of the ventricles andthe left-ventricular myocardium was studied. First,EDV, ESV, and the volume of myocardium were com-
Figure 3. Schematic 2D representation of interactive defor-mation using a software tool. The solid light gray contoursrepresent surfaces before deformation and black dotted con-tours after deformation. The user places a sphere on data (lightgray circle) and drags and drops a point in the center of thesphere (light gray arrow). Then the software tool computes asmooth deformation inside the sphere, based on the vectorfrom the drag-and-drop operation, and deforms all surfacepoints inside the sphere accordingly (black dotted contours).The user can change the location and size of the sphere freelyduring the interactive processing; the larger is the sphere, themore global is the transformation.
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puted from all five available segmentations. Then, foreach subject the differences between automatically andmanually generated volumes were computed, eg, ‘Auto’-‘Man A’ and ‘Auto’-‘Man B’ when the fully automaticsegmentation was evaluated. The agreement betweenthe volumes was expressed as the mean � standarddeviation of these differences. The t-test used indicateswhether a systematic difference exists in the volumes.The variances (the square of the standard deviation) ofthe agreements were also compared. We studied if thevariance between two manual measurements is differ-ent from the variance between manually and automat-ically defined measurements. The F-test was used (18).This test indicates if the variability between automaticand manual measurements is different from the inter-observer variability defined by the standard deviation ofthe difference between two manually extracted vol-umes.
Squared correlation coefficients, R2, were also com-puted. A 95% confidence interval was defined for acorrelation coefficient between two manual segmenta-tions using the Fisher transformation (18). If the corre-lation coefficient between manually and automaticallydefined volumes was outside of this interval, we con-cluded a statistically significant difference.
In Ref. (5) the difference between ED and ES left-ventricular myocardial volumes was used to estimatethe exactness of the segmentation. In the optimal situ-ation, no difference should be observed. The approachis based on experimental data showing that the LVmyocardium is incompressible throughout diastole andsystole (19). The finding has also been supported byechocardiography (20) and MRI (21). Our H0 hypothesisis that no difference exists.
In addition, the standard Dice similarity coefficients,measuring the overlap between two segmentations, wascomputed:
DICE �2�V � U��V� � �U� , [1]
where V and U are two segmented volumes to be com-pared. The value 1 indicates perfect overlap and 0 nooverlap.
Distance-Based Analysis
In volumetric analysis, local inaccuracies of segmenta-tions are averaged out. Exact delineation of contoursand surfaces is, however, important in computing var-ious, especially local, thickness- and motion-basedmeasures. The average distance between manually andautomatically defined triangulated surfaces was calcu-lated to characterize the exactness of the segmentationin millimeters. The closest distance was computed fromeach triangle node in the manual and automatic seg-mentations to the surfaces of the automatic and man-ual segmentations, respectively, and the average of allthese distances was defined. These distances were com-pared with corresponding distances between two man-ual segmentations using the t-test.
Visual Analysis
Because automatic methods have not yet reached thequality of human interpreters, clinicians must checkthe validity of results. Hence, visual goodness of seg-mentation has an important role when acceptability of atool is considered. Therefore, we also validated ourmethod visually by one cardiologist, one radiologist,and one technician with several years experience ininterpreting cardiac CMR images and different from thepersons who did the manual segmentation. From all 40cases one ED and ES short-axis slice from the levelwhere papillary muscles were visible, and one ED andES long-axis or transaxial slice from the center of theleft ventricle are shown (Fig. 4). The cine sequence ofthe original slice was simultaneously shown on thescreen for making evaluation more accurate. Five seg-mentations (‘Man A,’ ‘Man B,’ ‘Auto,’ ‘Auto�2min,’ and‘Auto�Nmin’) were presented side-by-side in a random-ized order to the evaluators. Segmentation of eachstructure was shown separately except that the epi-and endocardial borders of the LV were shown simul-taneously on long-axis data. The reason for this wasthat only the quality of segmentation on basal and api-cal regions was evaluated from the long-axis data (seeImage Analysis, above). Evaluators were asked to gradesegmentations by a number 1–5: 1 � excellent, 2 �acceptable, 3 � minor corrections needed, 4 � majorcorrections needed, and 5 � extreme correctionsneeded. Altogether, 2000 grades were given per evalu-ator. The grades of ‘Auto’ and ‘Auto�2min’ were com-pared with the grades of ‘Man A’ and ‘Man B’ using theWilcoxon rank sum test. The evaluation software wasdeveloped in-house specifically for this purpose withthe Microsoft .NET Framework 2.0 using the C# 2.0programming language.
RESULTS
Volumetric Analysis
The results are shown for LV, RV, and LV myocardialvolumes and the Dice coefficients in Table 2 and Bland–Altman plots (22) are shown in Fig. 5. The table showsthat automatic and semiautomatic segmentations (col-umns ‘Auto-Man’ and ‘Auto2-Man’) do not differ frommanual segmentation (column ‘Man-Man’) in estimat-ing volumes of both ventricles. In the LV the only sta-tistically significant difference indicates that fully auto-matic segmentation underestimates the volume by�2.9 mL (�4.2%) in the ES. On the other hand, semi-automatic segmentation gives higher correlation coeffi-cient for the ESV in RV. A clear difference can be seen inthe LV myocardial volumes.
If the segmentation was perfect the left-ventricularED and ES myocardial volumes should be equal. Themean difference and standard deviation of the differ-ence were 7.7 � 6,3, 7.6 � 5.7 and 7.6 � 5.5 mL for‘Auto,’ ‘Auto�2min,’ and ‘Auto�Nmin’ segmentations,respectively. In manual segmentations (‘Man A’ and‘Man B’) the difference was 8.5 � 5.9 mL. The differenceis not statistically significant.
Multiaxis Cardiac MR Segmentation 631
Distance-Based Analysis
Average distances between manually and automaticallydefined surfaces are shown in Table 3. For the endocar-dial border of the LV, the difference between automat-ically and manually generated surfaces was not differ-ent compared with the difference between two manuallygenerated surfaces. For the RV and the epicardial bor-der of the LV, the distance between manual segmenta-tions was smaller than between automatic and manualsegmentations. The distribution of distance values overthe ventricles is shown in Fig. 6.
Visual Analysis
The results of visual grading are shown in Fig. 7, whichalso shows whether the difference from manual seg-mentations is statistically significant. The differenceswere not dramatic. For example, 0.2 difference meansthat in every fifth image the grade is one step higher, eg,‘acceptable’ instead of ‘excellent.’
Computation Time
The breathing correction was the most time-consumingphase. The time needed varied from 8–90 seconds, de-pending on the number of slices and the size of correc-tion needed. The time needed for all the other opera-tions was 18 seconds, leading to a total computationtime of 26–108 seconds (standard laptop computer:Core2, 2.16 GHz).
DISCUSSION
A segmentation solution was proposed for segmentingLV, RV, and LV myocardium from multiaxis cardiacCMR images. The solution meets clinical requirementsregarding both quality and time.
Our study demonstrated that the results of the fullyautomatic method did not differ from manual segmen-tations for the endocardial border of the LV. In thevolumetry the only statistically significant differencewas the systematic underestimation of the ESV by �2.9mL (�4.2%). However, this value is only 30% of theinterobserver variability between the manual segmen-tations (standard deviation in the ‘Man-Man’ columnwas 9.0 mL, 13.2%). If all four experts had segmentedall 40 cases, we could also study the systematic inter-observer difference, ie, to which extent experts delin-eate surfaces in different ways systematically. Now, themean difference, �1.0 mL, shows only a random differ-ence because both groups ‘Man A’ and ‘Man B’ con-tained segmentations from all four experts equally. Forcomparison, we reordered the groups A and B in such away that the segmentations of the expert who made 32cases were moved to only one group and recomputedthe difference. The new mean difference was �3.7 mL,which was statistically significant. Therefore, the �2.9mL difference detected is within interobserver variabil-ity. On the other hand, we must keep in mind that astatistically significant difference is not a synonym for aclinically significant difference. If the number of cases
Figure 4. User interface for visual grading of segmentations. The images on the top row and bottom row show end-diastolic and-systolic phases of one slice from one patient when five different segmentations (‘Man A,’ ‘Man B,’ ‘Auto,’ ‘Auto�2 min,’ and‘Auto�N min’) was superimposed on images in random order. The user grades the images by clicking a desired grade next to eachimage.
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studied was increased enough, even a microliter scaledifference in ESV would become statistically signifi-cant, although such a difference would have no clinicalrelevance. No difference was detected in the distance-based measures either. Visual grading also producedvery comparable results.
The results of the left ventricular epicardial surfacewere conflicting. The results showed that manual seg-mentation was superior to the fully automatic methodin estimating volumes. An interesting result was that asimilar difference was found for the ‘Auto�Nmin’ seg-mentation, which is also a real manual segmentation.We believe that this conflict is due to a bias in thedistance between the epi- and endocardial borders inmanual segmentations. As the mean model was fitted todata, each surface was not deformed independently,but the model was treated in the beginning as a contin-uous physical object and it was deformed as wholeness(Fig. 3). Therefore, distance between surfaces that areclose to each other, such as epi- and endocardial bor-ders, did not change radically when the size of thedeformation sphere was large in the beginning. How-ever, in many cases visually satisfactory results wereobtained with large sphere sizes, which leads to anunderestimated value for the difference between ‘ManA’ and ‘Man B’ segmentations. Visual analysis also sup-ports this assumption. No clear difference was observedbetween the manual and fully automatic results. In thedistance-based measures, a small (about 0.2–0.25 mmcompared with average pixel size 1.5 � 1.5 mm) but astatistically significant difference was shown between
automatic and manual segmentations. In addition, thedifference between the EDV and ESV of the myocar-dium indicated no difference. For these reasons we arenot able to make final conclusions on the superiority ofmanual delineation in segmenting the left-ventricularepicardial border.
When the right ventricle is considered, no statisti-cally significant difference could be observed in thevolumetric measures. However, the distance-basedmeasures were clearly worse. The reason can be seenin Fig. 6, which shows larger error especially in theapex and a small difference (about 1 mm comparedwith average slice-separation 10 mm) in the base.Because the contribution of the apex to the totalvolume is small, this error was not seen in volumetricmeasures. The visual analysis supported these dis-tance-based observations: the visual quality wasworse for the RV than for the LV.
For the right ventricle the squared correlation coeffi-cients (R2) of the EF were very low, although the corre-lation coefficients of the EDV, ESV, and SV values wererelatively high (Table 2). We identified several reasonsfor the low values: 1) The segmentation of the RV ismore challenging and less standardized than the seg-mentation of the LV. 2) The correlation coefficient of theEF is more sensitive to small errors in the EDV and ESVthan the correlation coefficient of the SV. Bland–Altmanplots demonstrate the reason: the smaller the standarddeviation of the differences between measurements (y-axis) relative to the standard deviation of the average ofthe measurements (x-axis), the higher the correlation
Table 2Differences Between Automatic and Manual Measurements and the Standard Dice Coefficient
LV Auto-Man Auto2-Man AutoN-Man Man-Man Auto R2 Auto2 R2 AutoN R2 Man R2
The mean and standard deviation for the difference between volumes in milliliters and squared correlation coefficients. The mean is theaverage over both manual segmentations (A and B), ie, N�2*40, when automatically generated measures are compared with the manualsegmentations. As two manual segmentations are compared, N�40 and the sign of the difference has no meaning.LV, left ventricle; RV, right ventricle; Auto, automatic segmentation; Auto2, automatic segmentation with 2 minutes for corrections; AutoN,automatic segmentation with unlimited time for corrections; Man, manual segmentations A and B; EDV, end-diastolic volume; ESV,end-systolic volume; SV, stroke volume; EF, ejection fraction; EDDICE, Dice coefficient in end-diastole; ESDICE; Dice coefficient inend-systole. Correlation coefficients are denoted by R2.*Statistically significant (P � 0.05). The symbol after the mean indicates difference in the mean (t-test) and after the standard deviationdifference in the variance (F-test). The statistically significant differences for correlation coefficients were also computed (Fisher transfor-mation). The statistical significance was computed also for the Dice coefficients, ie, the coefficients calculated from manually generated vs.automatically generated segmentations.
Multiaxis Cardiac MR Segmentation 633
coefficient. The ratio was 1.47 for the EF and 0.62 forthe SV indicating the higher sensitivity of the EF touncertainties in the EDV and ESV values used to com-pute the EF and SV. 3) Although we need to estimatethe location of the base for more accurate volume esti-mates, the estimation also induces more variability, asit is challenging to make from CMR images. In addition,
only one long-axis slice was available for 50% of cases,which made the estimation even more challenging. Acareful standardization of the RV segmentation, includ-ing rules and information about optimal imaging direc-tions and number of slices for locating the base, isrequired for consistent estimation of the EF in the fu-ture.
Figure 5. Bland–Altman plot for manually and automatically defined volumes at the end-diastolic and end-systolic. The solidline shows the mean difference, and the dashed lines show the mean � 2 standard deviations.
Table 3Average and Standard Deviation for the Distance Between Segmented Surfaces in Millimeters
N � 2*40 when automatically generated segmentations were compared with both manual segmentations and N � 40 when two manualsegmentations were compared with each other.*Statistically significant (P � 0.05).
634 Lotjonen et al.
Automatic segmentation has been studied by severalresearch groups (7–11,23). The distance-based mea-sures for the end-diastolic phase was 1.8–2.8 mm, 1.9–2.8 mm, and about 2.1–2.3 mm for the endo- and epi-cardial borders of the LV and the endocardial border ofthe RV, respectively. In most cases the base has beenexcluded from the analysis. The corresponding values(Table 3) in our study were 1.4 mm, 1.5 mm, and 2.2mm, and without base 1.2 mm, 1.4 mm, and 1.9 mm,which shows the competitiveness of our method com-pared with previously published works.
When the results of semiautomatic segmentationwith 2 minutes user interaction were compared withthe result after unlimited time for user interaction, re-sults were very similar in all three analyses. This showsthat 2 minutes were enough for correcting the results.Two minutes interaction time was chosen as it wasconsidered to be clinically acceptable and to overcomecurrently available solutions. The comparison with in-dependent manual segmentations (‘Man A’ and ‘Man B’)revealed no difference except for the myocardium of theLV in volumetric analysis. However, we believe that thedifference was due to the bias in the manual segmen-tations.
Several questions still remain open: 1) The influenceof the multiaxis MR data on the segmentation was notstudied. It is likely that using comprehensive informa-tion from multiaxis images improves the geometric ac-curacy compared with the standard situation usingonly a sparse stack of short-axis slices. 2) Imaging pa-rameters and the characteristics of the disease haveobvious effects on the segmentation accuracy; however,a much larger database would be needed to allow adetailed analysis of the role of different parameters. 3)In this study we did not have enough data to study indetail the relative goodness of single-slice and multi-slice long-axis and transaxial data. These issues are
clinically highly relevant and must be clarified in futurestudies.
In conclusion, fully automatic segmentation can beused in the assessment of left ventricular endocardialborder, as a difference with manual segmentation wasnot observed. Some user interaction is needed to obtainaccurate results also for the epicardial border of the LVand for the endocardial border of the RV, although thenecessity of corrections for the LV was not clearlyproved. We showed that only 2 minutes is required toobtain satisfactory results. Special attention should bepaid in correcting the apical and basal parts of the RV.
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Figure 6. Distribution of distances between surfaces in milli-meters for two manual segmentations at end-diastolic (top-left), and end-systolic (top-right) phases, and for manual andautomatic segmentations at end-diastolic (bottom-left) andend-systolic (bottom-right) phases. The distances are averagesover all 40 cases.
Figure 7. Average values of grades for each segmentation andstructure at diastole and systole. Each bar is an average of 120grades (40 cases and 3 evaluators). The structures involved arethe endo- and epicardial borders of the left ventricle in short-or long-axis data (‘LV Endo SAx,’ ‘LV Epi SAx,’ and ‘LVEndo&Epi LAx’) and the endocardial border of the right ven-tricle in short-axis and long-axis data (‘RV Endo SAx’ and ‘RVEndo LAx’). The grades used were 1 � excellent…5 � extremecorrections needed. The statistically significant difference totwo manual segmentations is indicated by an asterisk abovethe corresponding bar.
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