

ORIGINAL ARTICLE 

Year : 2014  Volume
: 4
 Issue : 3  Page : 211222 

Right Ventricle Functional Parameters Estimation in Arrhythmogenic Right Ventricular Dysplasia Using a Robust Shape Based Deformable Model
Mostafa Ghelich Oghli^{1}, Vahab Dehlaghi^{1}, Ali Mohammad Zadeh^{2}, Alireza Fallahi^{3}, Mohammad Pooyan^{4}
^{1} Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah, Iran ^{2} Department of Radiology, Shaheed Rajaei Cardiovascular, Medical and Research Center, Tehran, Iran ^{3} Department of Biomedical Engineering, Hamedan University of Technology, Hamedan, Iran ^{4} Department of Biomedical Engineering, Shahed University, Tehran, Iran
Date of Submission  01Jul2013 
Date of Acceptance  04May2014 
Date of Web Publication  19Sep2019 
Correspondence Address: Vahab Dehlaghi Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah Iran
Source of Support: None, Conflict of Interest: None  3 
DOI: 10.4103/22287477.137840
Assessment of cardiac rightventricle functions plays an essential role in diagnosis of arrhythmogenic right ventricular dysplasia (ARVD). Among clinical tests, cardiac magnetic resonance imaging (MRI) is now becoming the most valid imaging technique to diagnose ARVD. Fatty infiltration of the right ventricular free wall can be visible on cardiac MRI. Finding rightventricle functional parameters from cardiac MRI images contains segmentation of rightventricle in each slice of end diastole and end systole phases of cardiac cycle and calculation of end diastolic and end systolic volume and furthermore other functional parameters. The main problem of this task is the segmentation part. We used a robust method based on deformable model that uses shape information for segmentation of rightventricle in short axis MRI images. After segmentation of rightventricle from base to apex in end diastole and end systole phases of cardiac cycle, volume of rightventricle in these phases calculated and then, ejection fraction calculated. We performed a quantitative evaluation of clinical cardiac parameters derived from the automatic segmentation by comparison against a manual delineation of the ventricles. The manually and automatically determined quantitative clinical parameters were statistically compared by means of linear regression. This fits a line to the data such that the rootmeansquare error (RMSE) of the residuals is minimized. The results show low RMSE for Right Ventricle Ejection Fraction and Volume (for RV EF, and mL for RV volume). Evaluation of segmentation results is also done by means of four statistical measures including sensitivity, specificity, similarity index and Jaccard index. The average value of similarity index is 86.87%. The Jaccard index mean value is 83.85% which shows a good accuracy of segmentation. The average of sensitivity is 93.9% and mean value of the specificity is 89.45%. These results show the reliability of proposed method in these cases that manual segmentation is inapplicable. Huge shape variety of rightventricle led us to use a shape prior based method and this work can develop by fourdimensional processing for determining the first ventricular slices. Keywords: Arrhythmogenic right ventricular dysplasia, deformable model, functional parameters, segmentation, shape prior
How to cite this article: Oghli MG, Dehlaghi V, Zadeh AM, Fallahi A, Pooyan M. Right Ventricle Functional Parameters Estimation in Arrhythmogenic Right Ventricular Dysplasia Using a Robust Shape Based Deformable Model. J Med Signals Sens 2014;4:21122 
How to cite this URL: Oghli MG, Dehlaghi V, Zadeh AM, Fallahi A, Pooyan M. Right Ventricle Functional Parameters Estimation in Arrhythmogenic Right Ventricular Dysplasia Using a Robust Shape Based Deformable Model. J Med Signals Sens [serial online] 2014 [cited 2023 Feb 9];4:21122. Available from: https://www.jmssjournal.net/text.asp?2014/4/3/211/137840 
Introduction   
Arrhythmogenic right ventricular cardiomyopathy/arrhythmogenic right ventricular dysplasia (ARVC/ARVD) is an inherited disease that presents with sustained ventricular tachycardia. ARVD characterized by a total or partial replacement of myocardium, especially the rightventricle myocardium, by fibroadipose tissue, which may be diffuse. This kind of cardiomyopathy shows a marked dilatation with an alteration of the regional kinetic. ^{[1],[2]} Although incidence and prevalence of ARVD are unknown, ARVD is recognized as a major cause of sudden death in young adolescents, and in one series it accounted for 20% of sudden deaths in all individuals younger than 35 years and 22% of sudden deaths in young athletes. ^{[1]} In early stages, the dysfunctions may be subtle and the diagnosis is quite difficult. On the contrary, in advanced stages, right ventricular (RV) enlargement may be evident as well as various clear clinical signs. ^{[3]} It is important to suspect any disorder in the early stages since sudden death can occur, especially in the subjects who present premature ventricular complexes or ventricular tachycardia originating from the RV. Diagnosis of ARVD needs a number of clinical tests, including the electrocardiogram (ECG), echocardiography, cardiac magnetic resonance imaging (MRI), and genetic testing. Like other kind of cardiac disorders, these tests are not perfect individually and their findings should be gathered to make a reliable diagnosis. Here, are some of clinical tests of ARVD description.
Electrocardiogram
About 90% of individuals with ARVD have some ECG abnormality. The most common ECG abnormality seen in ARVD is T wave inversion in leads V1V3. However, this is a nonspecific finding, ^{[4],[5]} and may be considered a normal variant in right bundle branch block. ^{[6]}
Echocardiography
Echocardiography may reveal an enlarged, hypo kinetic rightventricle with a paperthin RV free wall. The dilatation of the RV will cause dilatation of the tricuspid valve annulus, with subsequent tricuspid regurgitation. Paradoxical septal motion may also be present. However the echocardiography is cheaper and more access able, diagnosis of ARVD in echocardiography in early stages is a challenging task and almost impossible in many cases. ^{[7]}
Cardiac Magnetic Resonance Imaging
Cardiac MRI is a noninvasive imaging modality, which can be perfectly customized for each patient; furthermore, with the increased time and spatial resolutions, it provides perfect images for a complete overview of the rightventricle (RV). ^{[8]} In facts, it allows an anatomic, functional and morphologic approach, so that it is possible to suspect several disorders despite the complex crescent shape of the RV. Cardiac MRI is now becoming the most valid imaging technique to diagnose ARVD. ^{[9]} Cardiac MRI can visualize the extreme thinning and akinesis of the RV free wall. However, the normal RV free wall may be about 3 mm thick, making the test less sensitive. One of the most advantages of cardiac MRI is its capability to show more details on a single image; therefore, it is possible to detect both papillary muscles and trabeculae. Those little parts are suspected to become hypertrophied in case of ARVD. ^{[10]}
Researches show that among these techniques, MRI allows the clearest visualization of the heart. ^{[11]} Because, as mentioned, MRI depicts both functional and structural abnormalities, positive MR imaging findings should be used as important additional criteria in the clinical diagnosis of ARVD. This fact can be inferred and is mentioned in texts that nowadays Cardiac MRI is the gold standard for assessing RV volume. ^{[9]} The wall motion analysis, which is very important in the early stages of ARVD, is still assessed visually so that even experienced operators can miss subtle abnormalities. ^{[12]} One of the most important and valuable findings in clinical tests of a patient with ARVD is cardiac functional parameters including rightventricle end diastolic volume (RVEDV), rightventricle end systolic volume (RVESV), ejection fraction (EF) and cardiac output (CO). Assessment of these parameters is now done by means of cardiac MRI and echocardiography. However, echocardiography due to its disability to visualize rightventricle borders, especially in case of arrhythmic beating and dilated RV (present in ARVD) is not a suitable choice for assessment of cardiac functional parameters. To estimate these parameters, it is necessary to apply segmentation methods at each slice. The segmentation of enddiastolic (ED) and endsystolic (ES) images of the RV is currently performed manually in clinical routine. This long and tedious task, prone to intra and interexpert variability, requires about 20 min/ventricle by an expert clinician. In addition, in case of ARVD the wall motion abnormality makes the border of rightventricle unclear and this makes the segmentation procedure more complicated. The great need for automated methods has led to the development of a wide variety of segmentation methods. ^{[13]} Most of these methods compute a pixel wise correspondence between the current image (or frame) and model distributions of photometric (intensity based) and geometric properties of the target objects. In a general view, methods can be categorized in thresholding, ^{[14],[15],[16]} pixel classification, ^{[17],[18],[19],[20]} deformable models, Active Shape and Appearance models ^{[21],[23]} and Atlas ^{More Details} based segmentation. Among these methods, deformable models have been greatly used as their flexibility, especially for this application, ^{[2428]} either on the form of twodimensional active contours or threedimensional deformable surfaces. A review of papers on deformable models can be found in. ^{[29]} Despite of great advantages and wide range of researches use gradient based active contours; these types of deformable model methods are highly sensitive to the presence of noise and poor image contrast, which can lead to bad segmentation results. To overcome this drawback, some authors have incorporated robust regionbased evolution criteria into active contour energy functional built from intensity statistics and homogeneity requirements. ^{[30]} Chan and Vese method ^{[31]} is one of the most important of such methods that is based on techniques of curve evolution, MumfordShah (MS) functional. These methods can deal with problems that mentioned in the primary deformable model methods, but they are not able to deal with occlusion problems or presence of strongly cluttered background. ^{[30]} Therefore, the integration of prior shape knowledge about the objects in the segmentation task represents a natural way to solve occlusion problems. Rightventricle segmentation methods that are gathered in review papers, ^{[11],[32]} mostly use strong prior information like atlas based methods, active shape and appearance models, electromechanical models, etc., However, using shape prior information in curve evolution equation, however a classic method is now, is not considered for segmentation of rightventricle in cardiac MRI images. The aim of this study is performing a robust shapebased deformable model, described in ^{[30]} on cardiac MRI images of a patient with ARVD to estimate functional parameters of rightventricle. To achieve this aim, first, rightventricle should be segmented from cardiac MRI images. Second, volume estimation procedure should be applied to estimate rightventricle volume in ED and ES phases. And finally, other rightventricle functional parameters like EF and CO should be obtained by means of their formula. The following sections of this paper describe the shapebased deformable model method, application of this method for segmentation of cardiac rightventricle and calculation of RVEDV, RVESV and EF based on segmented areas. The validation of results is performed in final section.
METHODS
In practice, the point is not computed. It corresponds to the closest point of C (q) on the zero level set of and we used it to illustrate the shape function at point C (q). Indeed, the shape function is equal to the distance, that is, the value of the level setbased function at the point C (q). Finally, is obtained by integrating along the active contour, which defines the shape similarity measure equivalent to the sum of square differences. The minimization of allows us to increase the similarity between the active contour and the shape model. The functional is minimized using the calculus of variations and the gradient descent method which provide three flows acting on the curve C, the vector of Eigen coefficients x_{PCA} and the vector of geometric transformations x_{T} . Each of three flows can be analyzed by fixing the two others. w.r.t the curve C (classical geodesic flow), w.r.t vector of Eigen coefficients x_{PCA} and w.r.t vector of geometric transformation x_{T} . Analysis of by means of these three flows can be expressed in a variational level set formulation as presented in ^{[34],[36]} because the level set approach of these methods can be used to prove the existence of solution minimizing our energy functional in the space of functions with bounded variation. The level set formulation of the shape functional from Eq. 3 is:
As it mentioned in pervious section, there is a solution for solving these equations that will be discussed in section 23.
The Boundary Term
As it can be seen in "[Figure 4]," using shape and region force leads the curve to a predefined shape and homogeneous region and these forces cannot handle local structure variations.  Figure 4: Examples of not perfect segmentation results of using active contour in absence of boundary force and under region and shape force
Click here to view 
The model has not captured the local edge variations since it only deals with global shape variations provided by the PCA model. The model should be able to capture the local variations around the global shape; hence, adding local criteria to our energy functional is needed. We will consider for this purpose the classic geodesic active contour given by. The formulation of is mentioned in Eq. 4 and propose an energy function that leads our curve to the boundaries of RV.
A Numerical Solution
The authors in ^{[30]} obtained a system of coupled evolution equations whose steadystate solution gives the minimum of F, which means the solution of the segmentation problem. The functional F is expressed in the Eulerian/level set framework as follows:
• Level set function evolution at each iteration by means of the fast marching method that is presented in. ^{[40]}
This method robustly segments rightventricle in cardiac short axis MRI images for patients with ARVD from base to apex. This can be seen in "[Figure 5]." The result of using two groups of principal components for basal and apical slices and midventricular slices in implementation of this method can be seen in first and second row of "[Figure 6]."  Figure 5: Segmentation of right ventricular from (a) base to (l) apex for a 34yearold patient with arrhythmogenic right ventricular dysplasia. Red contour: Initial contour; green contour: Segmentation result
Click here to view 
 Figure 6: Result of segmentation of rightventricle in cine magnetic resonance imaging for patients with arrhythmogenic right ventricular dysplasia in (ad) Midventricular slices and (eh) Apical and basal slices. These two groups used two principal components. Red contour: Initial contour; green contour: Segmentation result
Click here to view 
Calculation of Functional Parameters
A number of parameters are used to evaluate ventricular (LV and RV) function. Volumetric measurements of the ventricles are performed on images obtained at ED and ES. ED and ES are determined visually as the cardiac phases that yield the maximum (enddiastolic volume [EDV]) and minimum (endsystolic volume [ESV]) RV volumes. These two volumes can then be used to determine the EF. The EF, defined as the proportion of blood ejected with each right ventricular (RV) contraction and mathematically is calculated by dividing subtraction of ESV from the EDV by the EDV. The advent of advanced tomographic techniques, such as cardiovascular magnetic resonance (CMR), has allowed for the accurate quantification of ventricular volumes. This method relies on Simpson's rule, which simply divides a large volume into smaller, more accurately measurable segments that are then summed. Compared with the geometric assumptions that are made with the modified Simpson's rule, a technique generally used with modalities such as echocardiography and radioscintigraphy, volumetric measurements using Simpson's rule in CMR are more accurate and have superior intra and interobserver reproducibility. On the basis of Simpson's rule, the segmented pixels of all images are counted and multiplied by their voxel size and the voxel size is defined as pixel_spacing_x * pixel_spacing_y * slice_thickness. Hence, volume of the rightventricle is determined through contiguous short axis slice models by:
Result   
This algorithm is tested on 30 cardiac short axis MRI images for patients with ARVD in size of 347 × 510 and obtained acceptable results. The implementation of this method is done using MATLAB 7.12.0.635 (R2011a) with License Number: 161052. Natick, Massachusetts, U.S.A.. [Figure 7] shows a visual comparison between our method and manual segmentation. Anterior wall and lateralbasal wall missegmentation in manual segmentation of some slices is observable in this "[Figure 7]".  Figure 7: Comparison of proposed algorithm segmentation results with manual segmentation. Top row: Manual segmentation by an expert radiologist (missegmentation of anterior wall and lateralbasal wall in some slices is obvious). Bottom row: Proposed algorithm segmentation results
Click here to view 
To validate the segmentation results and qualitative comparison, we compared obtained results with manual segmentation performed by a senior radiologist. [Table 1] shows cardiac functional parameters of 10 numbers of tested data that are obtained by segmentation of RV by introduced method. We performed a quantitative evaluation of clinical cardiac parameters derived from the automatic segmentation by comparison against a manual delineation of the ventricles performed on twodimensional shortaxis slices by a radiologist with experience in cardiac MR imaging. The manual delineation was a regular clinical quantification performed at the Shahid Rajaie Hospital a Philips EasyVision workstation, on patients with suspected ARVD. The manually and automatically determined quantitative clinical parameters were statistically compared by means of linear regression. This fits a line to the data such that the rootmeansquare error (RMSE) of the residuals is minimized , where SSR is the sum of squared residuals, and n is the number of data points. The comparative quantitative results for RV EF, and rightventricle volume are shown in "[Figure 7]." These plots indicate how well the automatic segmentation (X axis) can predict the volumes and EFs obtained using the manual expert segmentation (Y axis). The linear regression has a low RMSE (≤0.06 for RV EF, and ≤10 mL for RV volume). Evaluation of segmentation results is also done by means of four statistical measures contain sensitivity, specificity, similarity index (SI) and Jaccard index (JI). If M denote segmented region by an expert radiologist and A denote segmented region by means of our proposed algorithm, then these four statistical measures will define simply.
· Sensitivity relates to the test's ability to identify a condition correctly and it is defined by formula:
[Table 2] shows evaluation of the segmentation of RV on 30 datasets. Results show that this algorithm can robustly segments RV on MR cardiac images.  Table 2: Evaluation of segmentation results by means of four measures contain SI, JI, SE and SP for which an expert radiologist segmentation was available
Click here to view 
Discussion   
Cardiac MRI due to its capabilities in imaging a complete overview of the rightventricle is becoming a more and more important helpful means in diagnosing ARVC/ARVD. ARVD is a progressive disease leading to RV failure and several dysfunctions. Rightventricle functional parameters play an essential role in diagnosis of ARVD. Assessment of these parameters needs segmentation of rightventricle at each slice of cardiac MRI images. Unclear border of rightventricle in Cardiac MRI images of patients with ARVD is a huge difficulty in the way of segmentation methods and that is because of existence of cardiac arrhythmic beating in these patients. Level set techniques are by now wellknown and used in many tools, in fact it is impossible to segment complex shapes as RV without making geometrical assumptions. But using only contour information leads the curve to undesired results. So regards to complexities of rightventricle segmentation, region and shape information of rightventricle is added to the equation assumptions in this paper. Rightventricle segmentation methods mostly use strong prior information such as atlas based methods, active shape and appearance models, electromechanical models etc., However using shape prior information in curve evolution equation; however, is now a classic method, is not considered for segmentation of rightventricle in cardiac MRI images. Our aim was using a shape based deformable model for segmentation of rightventricle in cardiac short axis MRI images for patients with ARVD. Shape prior information was added to the equations by means of PCA. PCA aims at capturing the main variations of a training set while removing redundant information. However, there was a problem in preparing shape prior information by PCA and that was the huge shape variety of rightventricle that comes from position of MRI slices along the apexbase axis. For fixing this problem, principal components provided in two groups that was basal and apical slices and midventricular slices. Choosing between these two shape information terms was performed manually. Three force terms was used for curve evolution in the proposed method containing shape term, region term and boundary term that are gathered in an equation called energy function. Each term of this equation was weighted by a positive constant. And all parts of energy function were solved by numerical solutions. The main goal of numerical solution is minimizing energy function that leads the curve to a homogenous region (regards to region term), borders of rightventricle (regards to boundary term) and a predefined shape of rightventricle (prepared by PCA regards to shape term). Future researches can develop this work by defining a parameter for choosing shape information terms or fourdimensional processing of short axis cardiac MRI images for determining the first ventricular slices [Figure 8].  Figure 8: Rightventricle ejection fraction (left plot) and volume (right plot), determined by the human expert and by the automatic segmentation for 30 exams. The root mean square error is expressed in the same units as the ejection fraction and volume
Click here to view 
Acknowledgments   
This paper is prepared from thesis No. 92208 that is done in Kermanshah University of Medical Sciences as a Ms thesis.
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Authors   
Mostafa Ghelich Oghli, PhD student of Biomedical Engineering in Isfahan University of Medical Sciences, Isfahan, Iran. He received a B.Sc. degree in Biomedical Engineering from Department of Biomedical Engineering, Shahed University, Tehran, Iran, and he received his M.Sc. degree in Biomedical Engineering from Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah, Iran. His research interest is Medical Image Analysis, especially Cardiac MRI Image Analysis.
Dr. Vahab Dehlaghi was born on April 30, 1973 in Sahneh, Kermanshah, IRAN. He received a B.Sc. in Mechanical Engineering in 1995 from Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran and his M.Sc. and PhD degrees in Biomedical Engineering in 1998 and 2007 from Sharif University of Technology and Amirkabir University of Technology respectively. He has a PostDoctoral research fellow from Biomechanics laboratory, Biomedical Engineering Department, Erasmus MC, Rotterdam, Netherlands. Since 2007, He has been assistant professor in Biomedical Engineering Department, at Kermanshah University of Medical Sciences, Kermanshah, Iran. His research area is CFD in biological systems and cardiovascular hemodynamics.
Dr. Ali Mohammad Zadeh was born in 1976. He graduated MD from Shaheed Beheshti University in 2003 and he graduated degree in Radiology from Shaheed Beheshti University in 2010. Since 2010 he has been assistant professor of Iran University of Medical Sciences. His research interests are in the field of Radiology, Doppler Ultra Sonography, CT Angiography and Interventional Radiology.
Alireza Fallahi, lecturer in Biomedical Engineering Department, Hamedan University of Technology, Hamedan, Iran. He received a B.sc. degree in Electrical Engineering from Tabriz University at 2007 and M.Sc. degree in Biomedical Engineering from Shahed University at 2010. His research interests are Medical Signal and Image Processing and fMRI.
Dr. Mohammad Pooyan, Associate professor in Biomedical Engineering Department, Shahed University, Tehran, Iran. He received his B.Sc. and M.Sc. degree in Electronics Engineering from Shiraz University and Tarbiat Modares University respectively. He has a PhD in Biomedical Engineering from Tarbiat Modares University. His current research interests are in the field of Medical Image Processing, Cardiac Signal Analysis, Bioinstruments and Microprocessors.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8]
[Table 1], [Table 2]
