Comparison of Segmentation Algorithms for Detecting Myocardial Infarction Using Late Gadolinium Enhancement Magnetic Resonance Imaging

Yibo Sun ,,Dongdong Deng ,2,,Liping Sun ,Yi He ,Hui Wng nd Jinzeng Dong ,

1 Department of Cardiology,The First Aff iliated Hospital of Zhengzhou University,Zhengzhou,Henan,China

2 School of Biomedical Engineering,Dalian University of Technology,Dalian,Liaoning,China

3 Department of Cardiology,Beijing Anzhen Hospital,Capital Medical University and National Clinical Research Center for Cardiovascular Diseases,Beijing,China

4 Department of Cardiology,Beijing Friendship Hospital,Capital Medical University,Beijing,China

Abstract

Keywords:magnetic resonance imaging; myocardial infarction; automatic method

Introduction

Ventricular tachycardia is a life-threatening heart disease that occurs frequently in patients with myocardial infarction (MI) [1].The presence of MI has an important prognostic and therapeutic value for predicting ventricle remodeling and cardiac dysfunction [2].Late gadolinium enhancement (LGE)magnetic resonance imaging (MRI) is the standard imaging technique used for MI detection [3,4].It has many advantages compared with computed tomography and echocardiography [4],and has become the f irst choice for detecting myocardial f ibrosis [5,6].

Although LGE MRI is widely used in a clinical setting to detect the location,transmurality,and composition of an infarct [7],there is no recognized optimal method of LGE quantif ication [8].Numerous methods for segmenting infarct tissues in the left ventricle have been devised.Of these,the most frequently used are threshold based,such as the full width at half maximum (FWHM) method[9] and thenstandard deviations (nSD) method[10].Although they are widely used in the clinic and in research,they require manual intervention,which is subjective and ineff icient.Several semiautomatic and automatic methods have been proposed[11,12],but most still require manual input or postprocessing,which hinders eff icient image processing for a large number of patients.

The aim of this study was to validate the accuracy of a new automatic segmentation method and compare its performance against that of thenSD and FWHM methods for scar segmentation.

Methods

Image Acquisition

Cardiovascular magnetic resonance (CMR) images from 26 diseased hearts were collected from Beijing Anzhen Hospital.Basic information on the patients is given in Table1.This study was approved by the Institutional Review Board of Beijing Anzhen Hospital,and informed consent was obtained from all participants.The detailed image acquisition protocol was published previously [13].Brief ly,CMR scans were performed with a 1.5 T scanner (Sonata,Siemens,Erlangen,Germany) with chest electrocardiogram gating and breath-hold techniques.The contrast agent was injected via an ulnar vein under high pressure,and late imaging was performed 15 min later.The scanning layer thickness was 8 mm or 10 mm with a f ield of view between 320 × 320 mm2and 340 × 340 mm2.The f inal inplane image resolution was between 1.4 mm and 1.75 mm.

Image Processing

All analyses and measurements were performed with custom software developed in MATLAB (The MathWorks,USA).For all LGE CMR images,the epicardial and endocardial boundaries in every two-dimensional slice were manually segmented by one experienced expert.The papillary muscles were excluded from the endocardium.Then pixels between the epicardial and endocardial boundaries were segmented bynSD thresholding,FWHM thresholding,and our new automatic method (see the third paragraph of this section).

For thenSD method,observers with 1,3,and 8 years of image segmentation experience selected a region of interest (ROI) far from the enhanced area.Then the mean and the SD of the selected ROI were calculated,and pixels with an intensity greater thannSD above the mean were def ined as infarcttissue.The value of n was selected as 2,4,6,8,or 10.For the FWHM method,the same three people selected one enhanced area,the maximal intensity of the selected enhanced area was calculated,and the pixels with intensity greater than half the maximal value were segmented as infarct tissue.For both the n SD method and the FWHM method,further manual improvements to the segmented images were made,including the removal of pixels not connected to the infarct tissue.

Table1 Characteristics of the Study Population.

The automatic algorithm developed by our group uses a classif ication method based on the Gaussian mixture model to segment the tissue inside the epicardial and endocardial boundaries.The Gaussian mixture model assumes a Gaussian distribution of the image intensity of each f itted class (in our case noninfarct tissue and infarct tissue) [14,15],each class having its own mean intensity μ and variance σ2.The maximal intensity (Imax) and minimal intensity (Imin) of the pixels between the epicardium and endocardium were calculated,and those pixels with intensity greater than 0.3 (Imax－ Imin) were designated as containing infarct tissue.Next,the regions in each image layer with more than 15 pixels were retained to remove small clusters of pixels affected by noise or blood vessels.The maximal component in each layer and the components with pixel intensities greater than 15% of the maximal components were then designated as the f inal infarct tissue to remove numerous pixels containing fat tissue or artifacts.

Evaluation Metrics

After analysis of the entire stack of ventricular images,the Dice and volume difference metrics were used to evaluate the segmentations.The Dice score represents the overlap between the ground truth (the segmentation of scar tissue performed by two experts with more than 10 years of CMR image segmentation experience) and the segmentation produced by the other algorithms described earlier.The volume difference measures the difference between the infarct volumes measured with the ground truth and the other segmentation algorithms.

Results

The segmentations obtained with the n SD method,the FWHM method,and the automatic method were compared with the consensus ground truth and their accuracy was measured by the Dice score.Without any manual intervention,our automatic segmentation method produced the highest Dice score (0.8)and the smallest variation in the Dice score for each image dataset (Figure1A).The 6SD and 8SD methods yielded a mean Dice score of 0.72,and the variation of the Dice score for each image dataset was much greater than that for the automatic method.The FWHM method had a mean Dice score of 0.64,and the variation of the Dice score for each image dataset was comparable with that for the automatic method.

After manual removal of pixels that were not connected to the infarct tissue,the Dice scores produced with the n SD and FWHM methods were increased(Figure1B).The 6SD and 8SD methods produced mean Dice scores of 0.79 and 0.77,respectively,which were close to the value obtained with the automatic method.The Dice score obtained with the FWHM method increased minimally from 0.64 to 0.65.

To further evaluate the accuracy of the infarct tissue segmentation,we also compared the volume difference between the consensus ground truth and the three segmentation methods (Figure1C and D).Our automatic method produced the smallest volume differences,with a median value of 0.70 mL.Before manual modif ication of the segmented images (Figure1C),the median values of the volume differences produced by the 6SD and 8SD methods were 1.31 mL and 1.09 mL,respectively.The FWHM method yielded a median volume difference of 1.47 mL.After manual modif ication of the segmented images (Figure1D),the median values of the volume differences obtained with the 6SD and 8SD methods decreased to 0.81 mL and 0.71 mL,respectively.Manual intervention increased the median volume difference obtained with the FWHM method to 1.68 mL.

Figure1 :Segmentation accuracy of the automatic method and of individual observers using other segmentation methods.Dice scores and volume differences were calculated for every region of scar tissue found in the consensus segmentation.

For different observers,large variations in the Dice score and the volume difference were obtained,especially before manual adjustments were made.The automatic method did not require any manual intervention for scar segmentation and thus had the highest reproducibility.The n SD method produced large variations in both the highest Dice scores and the lowest volume differences.For observers 1 and 3,the highest Dice score was obtained with the 6SD method,whereas the 10SD method yielded the highest Dice score for observer 2.The Dice score obtained with the FWHM method also varied for each observer.After adjustment of the segmented images,the 6SD method yielded the highest Dice score for all three observers.Before manual intervention,the 8SD method produced the lowest volume difference for observers 1 and 3,whereas the 10SD method resulted in the lowest value for observer 2.After manual adjustment of the image segmentation,the 8SD method produced the lowest volume difference for observers 1 and 2,while the 6SD method yielded the lowest value for observer 3.

Figure2 shows different observers’ segmentations of one patient’ s CMR images.The “ manual”panel shows the manual segmentation of one slice,which was treated as the ground truth segmentation.The “ automatic” panel shows the segmentation of scar tissue obtained with our automatic method.The result was very close to the consensus ground truth,which can be attributed to the eff icient removal of artifacts by the automatic method.The f irst row in each observer’ s segmentation in Figure2 shows the scar segmentation produced with thenSD method(n= 2,4,6,8,and 10) and the FWHM method.The second row shows the modif ied segmentation obtained after manual removal of pixels disconnected from the infarct tissue.For all observers’segmentations,the 2SD,4SD,6SD,and 8SD methods needed manual intervention to remove artifacts associated with fatty tissue on the epicardium.The 10SD method sometimes omitted some scar tissue from the segmentation (observer 3),whereas the FWHM method excluded some scar tissue in all observers’ segmentations.

Figure2 :FWHM Full Width at Half Maximum; SD Standard Deviations.

Discussion

Comparison of the three methods for segmentation of infarct tissue in the ventricle showed that our automatic method produced the highest Dice score and the lowest volume difference compared with the consensus ground truth segmentation.The automatic method does not require any manual intervention and therefore produces highly reproducible and objective results.ThenSD method resulted in large variations in the Dice score and volume difference for all values ofnstudied.Although the 6SD and 8SD methods yielded relatively good results,they relied on manual identif ication and removal of isolated pixels or artifacts.Furthermore,the segmentation of different patients’ images differed markedly between different observers,making thenSD method highly variable and unobjective.The FWHM method resulted in the lowest Dice score and highest volume difference compared with the automatic,6SD,and 8SD methods,but was less variable when different observers segmented the images.

Both thenSD method and the FWHM method have been widely used for clinical image segmentation [10,16].Our results are consistent with those of earlier studies in which the 6SD method(n= 2- 6) produced the highest Dice scores [11] and the FWHM method yielded lower Dice scores [12,13].Compared with thenSD method,the FWHM method has greater reproducibility.However,both methods need manual input to select the ROIs for threshold computation and for artifact removal [11].

Several automatic or semiautomatic methods are used to segment infarct tissue [11].The advantage of our method is that it is fully automated,which is desirable when imaging datasets from a large number of patients are being processed.However,our method is still limited by false segmentation introduced by noise,fatty tissue,or artifacts caused by an implantable cardioverter-def ibrillator.In addition,although our method is fully automatic with respect to scar segmentation,it still requires manual segmentation of the endocardium and epicardium in the left ventricle.

Conclusions

We compared three methods for segmenting CMR images containing infarct tissue in the ventricle.Our automatic method proved to be highly repetitive and objective,producing the highest Dice scores and the lowest volume differences compared with the consensus ground truth segmentation.The 6SD and 8SD methods yielded relatively good metrics,but they produced considerable variation in image segmentation by different observers and therefore lacked reproducibility and objectivity.Although segmentation by the FWHM method suffered from less variation between observers,this algorithm yielded the lowest Dice scores and highest volume differences.The automatic method is therefore highly recommended for segmenting ventricular scar tissue,and may be useful for processing large imaging datasets produced in the clinic.

Acknowledgments

This work was supported by grants from the National Key Research and Development Program of China (2016YFC1301002 to Jianzeng Dong)and the National Natural Science Foundation of China (81901841 to Dongdong Deng; 81671650 and 81971569 to Yi He).Dongdong Deng also acknowledges support from Dalian University of Technology (DUT18RC(3)068).We thank Liwen Bianji,Edanz Group China (www.liwenbianji.cn/ac),for editing the English text of a draft of this manuscript.