Segmentation Of Right Ventricle Research Articles

Subclinical cardiac impairments in fetuses conceived through assisted reproductive technology by speckle tracking echocardiography.

To evaluate the changes of cardiac morphology and function in fetuses conceived through assisted reproductive technologies (ART) by speckle tracking echocardiography. A retrospective study was conducted in 101 spontaneously conceived (SC) fetuses and 99 ART-conceived ones. Fetal echocardiography was performed, fetal cardiac morphology and function were analyzed using two-dimensional speckle tracking software, including global sphericity index (GSI), global longitudinal strain (GLS), fractional area change (FAC) of the left and right ventricles, as well as segmental sphericity index (SI), end-diastolic diameter (ED), and fractional shortening (FS) in 24 segments. Compared to the SC fetuses, the ART-conceived fetuses exhibited decreased GSI (median [interquartile range], 1.22 [1.16-1.27] vs. 1.18 [1.11-1.24], p=0.007), decreased right ventricular GLS (24.9 [21.5-27.6] vs. 23.2 [20.4-26.8], p=0.026), and decreased right ventricular FAC (mean±standard deviation, 39.7±6.4 vs. 37.2±7.1, p=0.003). Analysis of the 24 segments showed that ART-conceived fetuses had reduced SI in the apical segments of right ventricle and increased ED in several segments of the right ventricle. Fetuses conceived through ART had a more spherical shape of the global heart and predominantly right-sided cardiac remodeling and systolic function impairment.

Assessment of global and segmental strain in heterozygous APE1/Ref-1 transgenic mice using cardiac magnetic resonance imaging

Abstract Background Apurinic/apyrimidinic-endonuclease1(APE1/Ref-1) is a redox factor necessary for DNA binding and transcription regulator in ROS generation. The association between APE1/Ref-1 and heart failure(HF) has been observed in previous studies. Heterozygous APE1/Ref-1 transgenic mice displayed endothelial cell dysfunction with systemic hypertension. Global strain(GS) measures the myocardium's deformation along its axis and is known to be a sensitive marker of subclinical HF in hypertensive patients with preserved ejection fraction. Purpose This study aims to investigate the functional dynamics and GS of the heart in heterozygous APE1/Ref-1 mice to help identify the association between APE1/Ref-1 and early signs of HF and translate to clinical applications. Methods A study was conducted to assess the functional parameters of both the left ventricle(LV) and right ventricle (RV) and perform LV global and segmental strain analysis using cardiac magnetic resonance imaging(CMR) in heterozygous APE1/Ref-1 transgenic mice(n=4) and wild type mice (n=4) of age 10-12 weeks. A 7T CMR scanner and FSE T1 sequence were used to acquire short-axis views of mouse cardiac images. LV and RV functional parameters including ejection fraction(EF), stroke volume(SV), cardiac output, peak filling rate, peak emptying rate and strain indices including global circumferential strain, global radial strain, segmental circumferential strain, and segmental radial strain were calculated with a software dedicated to analyzing CMR images. Global LV and RV function was quantified both manually and using an AI-based algorithm of LV and RV segmentation. The strain analysis was performed using the strain medical image tracking toolbox module. Additionally, reproducibility testing was done to validate the accuracy of AI-driven calculations. Results We found that heterozygous APE1/Ref-1 mice displayed a decrease in SV(8.00±0.01ul vs 9.75±0.96ul, p=0.01) and LVEF(58.75±2.63% vs 66.50±2.89%, p=0.007) compared to controls while the functional parameters of RV were similar. Interestingly, the circumferential strain rate of LV was significantly reduced in APE1/Ref-1 mice compared to controls(p<0.001), particularly in the systolic phase. In addition, the study showed that manually-calculated and AI-derived indices were within an acceptable margin of error, rendering the AI system reproducible and reliable in calculating volumetric parameters and strain of the mice's hearts. Conclusions This study demonstrated that LV global circumferential strain rate, along with SV and LVEF decreased in heterozygous APE1/Ref-1 transgenic mice. These parameters could serve as markers for assessing early functional changes of mice hearts in a non-invasive manner. Volumetric and strain indices measured by AI-based analysis of mouse CMR images are reproducible parameters for cardiovascular research in small animals.LV strain analysis of mice CMRTable of volumetric parameters

Spatial-temporal V-Net for automatic segmentation and quantification of right ventricle on gated myocardial perfusion SPECT images.

Functional assessment of right ventricle (RV) using gated myocardial perfusion single-photon emission computed tomography (MPS) heavily relies on the precise extraction of right ventricular contours. In this paper, we present a new deep-learning-based model integrating both the spatial and temporal features in gated MPS images to perform the segmentation of the RV epicardium and endocardium. By integrating the spatial features from each cardiac frame of the gated MPS and the temporal features from the sequential cardiac frames of the gated MPS, we developed a Spatial-Temporal V-Net (ST-VNet) for automatic extraction of RV endocardial and epicardial contours. In the ST-VNet, a V-Net is employed to hierarchically extract spatial features, and convolutional long-term short-term memory (ConvLSTM) units are added to the skip-connection pathway to extract the temporal features. The input of the ST-VNet is ECG-gated sequential frames of the MPS images and the output is the probability map of the epicardial or endocardial masks. A Dice similarity coefficient (DSC) loss which penalizes the discrepancy between the model prediction and the manual annotation was adopted to optimize the segmentation model. Our segmentation model was trained and validated on a retrospective dataset with 45 subjects, and the cardiac cycle of each subject was divided into eight gates. The proposed ST-VNet achieved a DSC of 0.8914 and 0.8157 for the RV epicardium and endocardium segmentation, respectively. The mean absolute error, the mean squared error, and the Pearson correlation coefficient of the RV ejection fraction (RVEF) between the manual annotation and the model prediction were 0.0609, 0.0830, and 0.6985. Our proposed ST-VNet is an effective model for RV segmentation. It has great promise for clinical use in RV functional assessment.

Toward Right Ventricle Segmentation in Cardiac MRIs via Feature Multiplexing and Multiscale Weighted Convolution.

Cardiovascular diseases are the leading cause of mortality, and accurate segmentation of ventricular regions in cardiac magnetic resonance images (MRIs) is crucial for diagnosing and treating these diseases. However, fully automated and accurate right ventricle (RV) segmentation remains challenging due to the irregular cavities with ambiguous boundaries and mutably crescentic structures with relatively small targets of the RV regions in MRIs. In this article, a triple-path segmentation model, called FMMsWC, is proposed by introducing two novel image feature encoding modules, i.e., the feature multiplexing (FM) and multiscale weighted convolution (MsWC) modules, for the RV segmentation in MRIs. Considerable validation and comparative experiments were conducted on two benchmark datasets, i.e., the MICCAI2017 Automated Cardiac Diagnosis Challenge (ACDC), and the Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&MS) datasets. The FMMsWC outperforms state-of-the-art approaches, and its performance can approach that of the manual segmentation results by clinical experts, facilitating accurate cardiac index measurement for the rapid assessment of cardiac function and aiding diagnosis and treatment of cardiovascular diseases, which has great potential for clinical applications.

Characterization of right ventricular low voltage areas in patients with arrhythmogenic right ventricular cardiomyopathy

Abstract Funding Acknowledgements Type of funding sources: None. Background Arrhythmogenic right ventricular cardiomyopathy (ARVC) is characterized by fibrofatty replacement in the myocardium progressing from the epicardium towards the endocardium and affecting primarily the right ventricle (RV). Little data is available on the distribution of RV electrical abnormalities at the era of epicardial and high-resolution mapping. Purpose To evaluate the extent and distribution of RV epicardial and endocardial low voltage areas and their relationship with ECG abnormalities in ARVC patients. Methods Patients with a definite ARVC diagnosis according to the 2020 Task Force Criteria, who underwent ventricular tachycardia ablation between 2016 and 2022 with a high-density RV 3-dimensional electroanatomic mapping, were included. A custom RV segmentation dividing the RV into 10 segments from endocardial maps was used: anterior and lateral right ventricular outflow tract (RVOT); basal and mid inferior wall; apex; septum; basal anterolateral, basal inferolateral, mid anterolateral and mid inferolateral RV free wall (Figure). A standard definition of electrical scar was used (bipolar voltage ≤0.5 mV), and scar areas were manually delineated. Percentage of scar was defined by the ratio between scar area and the total segment area. ECG abnormal findings, such as epsilon wave and T-wave inversion in precordial leads, were measured before ablation procedure. Results Twenty-nine consecutive definite ARVC patients were included, among whose 24 had epicardial mapping in addition to endocardial mapping. When considering all segments, the epicardial scar (median:70%, interquartile range [IQR] 37-97%) was larger (p<0.0001) than the endocardial scar (9% [IQR] 0-32%). The higher percentage of epicardial scar was found in the basal inferolateral (100% [IQR] 73-100%), basal anterolateral (94% [IQR] 67-100%), RV free wall and lateral RVOT (89% [IQR] 48-100%) segments (Table). Transmural scar was observed mostly in basal inferolateral RV free wall (10 patients, 41%) and basal inferior wall (8 patients, 33%). Patients with major depolarization criteria had a higher percentage of epicardial and endocardial scar as compared to those with no or minor depolarization criteria (respectively 93% [IQR] 59-100% versus 62% [IQR] 28-89%, p<0.0001 and 17% [IQR] 0-42% versus 4% [IQR] 0-23%, p=0.001). There was no significant difference of the percentage of epicardial or endocardial scar in patients with and without major repolarization criteria (respectively 90% [IQR] 49-98% versus 84% [IQR] 43-100%, p=0.91 and 14% [IQR] 11-35% versus 16% [IQR] 0-41%, p=0.73). Conclusion In ARVC, the electrical abnormalities predominate in the basal epicardium. Patients with major depolarization criteria had a higher extent of epicardial and endocardial scar, but there was an association between scar extent and repolarization abnormalities.

Deep-active-learning approach towards accurate right ventricular segmentation using a two-level uncertainty estimation.

The Right Ventricle (RV) is currently recognised to be a significant and important prognostic factor for various pathologies. Its assessment is made possible using Magnetic Resonance Imaging (CMRI) short-axis slices. Yet, due to the challenging issues of this cavity, radiologists still perform its delineation manually, which is tedious, laborious, and time-consuming. Therefore, to automatically tackle the RV segmentation issues, Deep-Learning (DL) techniques seem to be the axis of the most recent promising approaches. Along with its potential at dealing with shape variations, DL techniques highly requires a large number of labelled images to avoid over-fitting. Subsequently, with the produced large amounts of data in the medical industry, preparing annotated datasets manually is still time-consuming, and requires high skills to be accomplished. To benefit from a significant number of labelled and unlabelled CMRI images, a Deep-Active-Learning (DAL) approach is proposed in this paper to segment the RV. Thus, three main steps are distinguished. First, a personalised labelled dataset is gathered and augmented to allow initial learning. Secondly, a U-Net based architecture is modified towards efficient initial accuracy. Finally, a two-level uncertainty estimation technique is settled to enable the selection of complementary unlabelled data. The proposed pipeline is enhanced with a customised postprocessing step, in which epistemic uncertainty and Dense Conditional Random Fields are used. The proposed approach is tested on 791 images gathered from 32 public patients and 1230 images of 50 custom subjects. The obtained results show an increased dice coefficient from 0.86 to 0.91 with a decreased Hausdorff distance from 7.55 to 7.45.

Toward Automated Right Ventricle Segmentation via Edge Feature-Induced Self-Attention Multiscale Feature Aggregation Full Convolution Network

In the field of cardiac magnetic resonance (MR) image analysis, the accurate segmentation of right ventricle (RV) regions plays an important role in the quantitative examination and medical diagnosis of various cardiovascular diseases. However, the automated RV segmentation in cardiac MR images is still challenging, due to its obscure and ill-defined boundaries, variably crescent-shaped structures, and extremely-unbalanced area ratio between the RV region and the background. In this work, an edge feature-induced self-attention multi-scale feature aggregation full convolutional neural network, called EFiSaMsFAFUnet, is proposed, to address the RV segmentation tasks. Specifically, EFiSaMsFAFUnet introduces an edge feature extraction module (EFEM) to mine the crescent-shaped boundary features of the RV area, and a kind of self-attention multi-scale feature expansion block (SaMsFEB) is proposed to mine the internal structure characteristics of the crescent-shaped RV internal regions. In addition, a kind of composite loss function is used to address the problem of model degradation caused by the proportion imbalance between RV region and background. The proposed EFiSaMsFAFUnet was evaluated on the MICCAI2017 Automatic Cardiac Diagnosis Challenge (ACDC) dataset. Extensive confirmatory and comparative experiments show that the EFiSaMsFAFUnet can achieve better segmentation results than the representatively state-of-the-art methods, and it can achieve comparable or the closest segmentation results to the manual segmentation of clinical specialist.

Right ventricular strain and volume analyses through deep learning-based fully automatic segmentation based on radial long-axis reconstruction of short-axis cine magnetic resonance images.

We propose a deep learning-based fully automatic right ventricle (RV) segmentation technique that targets radially reconstructed long-axis (RLA) images of the center of the RV region in routine short axis (SA) cardiovascular magnetic resonance (CMR) images. Accordingly, the purpose of this study is to compare the accuracy of deep learning-based fully automatic segmentation of RLA images with the accuracy of conventional deep learning-based segmentation in SA orientation in terms of the measurements of RV strain parameters. We compared the accuracies of the above-mentioned methods in RV segmentations and in measuring RV strain parameters by Dice similarity coefficients (DSCs) and correlation coefficients. DSC of RV segmentation of the RLA method exhibited a higher value than those of the conventional SA methods (0.84 vs. 0.61). Correlation coefficient with respect to manual RV strain measurements in the fully automatic RLA were superior to those in SA measurements (0.5-0.7 vs. 0.1-0.2). Our proposed RLA realizes accurate fully automatic extraction of the entire RV region from an available CMR cine image without any additional imaging. Our findings overcome the complexity of image analysis in CMR without the limitations of the RV visualization in echocardiography.

Anatomical knowledge based level set segmentation of cardiac ventricles from MRI

This paper represents a novel level set framework for segmentation of cardiac left ventricle (LV) and right ventricle (RV) from magnetic resonance images based on anatomical structures of the heart. We first propose a level set approach to recover the endocardium and epicardium of LV by using a bi-layer level set (BILLS) formulation, in which the endocardium and epicardium are represented by the 0-level set and k-level set of a level set function. Furthermore, the recovery of LV endocardium and epicardium is achieved by a level set evolution process, called convexity preserving bi-layer level set (CP-BILLS). During the CP-BILLS evolution, the 0-level set and k-level set simultaneously evolve and move toward the true endocardium and epicardium under the guidance of image information and the impact of the convexity preserving mechanism as well. To eliminate the manual selection of the k-level, we develop an algorithm for automatic selection of an optimal k-level. As a result, the obtained endocardial and epicardial contours are convex and consistent with the anatomy of cardiac ventricles. For segmentation of the whole ventricle, we extend this method to the segmentation of RV and myocardium of both left and right ventricles by using a convex shape decomposition (CSD) structure of cardiac ventricles based on anatomical knowledge. Experimental results demonstrate promising performance of our method. Compared with some traditional methods, our method exhibits superior performance in terms of segmentation accuracy and algorithm stability. Our method is comparable with the state-of-the-art deep learning-based method in terms of segmentation accuracy and algorithm stability, but our method has no need for training and the manual segmentation of the training data.

A cascaded FC-DenseNet and level set method (FCDL) for fully automatic segmentation of the right ventricle in cardiac MRI

Accurate segmentation of the right ventricle (RV) from cardiac magnetic resonance imaging (MRI) images is an essential step in estimating clinical indices such as stroke volume and ejection fraction. Recently, image segmentation methods based on fully convolutional neural networks (FCN) have drawn much attention and shown promising results. In this paper, a new fully automatic RV segmentation method combining the FC-DenseNet and the level set method (FCDL) is proposed. The FC-DenseNet is efficiently trained end-to-end, using RV images and ground truth masks to make a per-pixel semantic inference. As a result, probability images are produced, followed by the level set method responsible for smoothing and converging contours to improve accuracy. It is noted that the iteration times of the level set method is only 4 times, which is due to the semantic segmentation of the FC-DenseNet for RV. Finally, multi-object detection algorithm is applied to locate the RV. Experimental results (including 45 cases, 15 cases for training, 30 cases for testing) show that the FCDL method outperforms the U-net + level set (UL) and the level set methods that use the same dataset and the cardiac functional parameters are computed robustly by the FCDL method. The results validate the FCDL method as an efficient and satisfactory approach to RV segmentation.

Automated evaluation of the right-sided heart function and geometry using deep learning

Abstract Funding Acknowledgements Type of funding sources: None. INTRODUCTION Deep learning (DL) has been successfully applied in the automated assessment of some transthoracic echocardiography (TTE) parameters such as left-ventricular ejection fraction. Nevertheless, automation of the right-sided heart assessment has not been widely studied, partially due to the relative difficulty involved in some of the right-sided heart measurement evaluation and time constraints in routine practice. Here we have explored the feasibility of a DL-based system capable of performing different tasks involved in the right-sided heart functional and geometric evaluation. PURPOSE To develop a DL-based system assessing right atrium (RA) and right ventricle (RV) functional and geometric parameters and compare its accuracy to board-certified cardiologists. METHODS A total of 2,014 frames from 349 patients (with various indications for TTE) were used to train and validate four convolutional neural networks (CNNs) to perform either segmentation or landmark detection across four different TTE views: apical four-chamber (A4Ch), parasternal long-axis (PLAX), M-mode of tricuspid annulus and tissue Doppler imaging (TDI) of the right ventricular lateral wall. The CNNs were optimised to perform different right-sided heart measurements, namely, right atrial area in end-systole (RAA) and fractional area change (FAC) of RV in A4Ch view, proximal right ventricular outflow tract diameter (pRVOT) in PLAX view, tricuspid annular plane systolic excursion (TAPSE) in M-mode and S’ in TDI. Model performance was compared with two board-certified cardiologists using their average measurements on 20 test set patients. RESULTS CNN predicted pRVOT diameter with a mean absolute error (MAE) of 1.02 mm and root mean squared error (RMSE) of 3.08 mm. The intersection over union (IoU) for the segmentation of RV and RA was 0.89 and 0.87, respectively. We then used RV and RA segmentation predictions to calculate additional parameters which resulted in RMSE of 8.34% for FAC and 4.93cm2 for RAA. In the M-mode and TDI, the model achieved RMSE of 4.48 mm and 0.84 cm/s for the detection of TAPSE and S’, respectively. CONCLUSIONS We have demonstrated the feasibility of a DL-based system performing different measurements involved in right-sided heart evaluation. In a routine practice, where limited time resources might be available, such could assist in the thorough assessment of the right-sided heart geometry and function. Additional studies using cardiac magnetic resonance imaging to establish more precise accuracy of such systems is needed.

Assessment of right ventricular segmental volumes and ejection fractions using a 15-segment model: three-dimensional echocardiographic study in healthy volunteers

Abstract Funding Acknowledgements Type of funding sources: None. On top of global ventricular function, segmental metrics may bear clinically relevant information. Concerning the left ventricle (LV), standardized segmentation is widely performed in different cardiovascular imaging modalities mainly to correlate regional dysfunction with coronary perfusion territories, or to appreciate and quantify distinct patterns in LV myocardial function. The same applies to the right ventricle (RV); as pulmonary hypertension, or arrhythmogenic cardiomyopathy are just two clinical examples among several others, where established regional dysfunction exists. Nevertheless, only a few options are available for the comprehensive and quantitative assessment of the segmental RV function due to its complex three-dimensional (3D) shape. Therefore, our aim was to develop a 3D echocardiographic software solution for volumetric partitioning of the RV using a 15-segment model and to investigate a large number of healthy volunteers to describe the normal segmental pattern. One hundred and fifty healthy adults with a balanced age range and an equal sex distribution were investigated (15-15 women and men in each age groups: 20-29, 30-39, 40-49, 50-59, 60+). Beyond standard two-dimensional echocardiographic protocol, full volume 3D datasets were acquired. Using commercially available software, we reconstructed the 3D mesh model of the RV and measured end-diastolic (EDV), end-systolic volumes and ejection fraction (EF). The 3D model was post-processed using the ReVISION method to calculate regional and segmental volumes and EFs. Fifteen standard segments were separated and quantified (Figure). Increasing age resulted in significantly lower RV stroke volume (r=-0.17; p < 0.05) and tended towards lower RV EDV (r=-0.15, p = 0.06). EDVs of inflow tract and outflow tract segments decreased during aging (r=-0.21, p < 0.05 and r=-0.26, p < 0.01, respectively). Between the pre-specified age groups, there was no difference concerning global RVEF (ANOVA p = NS). In the 50-59 age group, regional EF of septal segments and also free wall segments were significantly lower compared to subjects in the 30-39 and 40-49 age categories (both p < 0.05). Global RV EDV was significantly lower in women (women vs. men: 95 ± 20 vs. 125 ± 28 ml; p < 0.05) along with a higher RV EF compared to men (62 ± 4 vs. 59 ± 4; p < 0.05). However, segmental EFs of apical, septal mid anterior, free wall mid posterior, free wall mid lateral, septal basal anterior and inflow tract segments were comparable between genders. The ReVISION method allows a volumetric partitioning of the RV 3D models to investigate segmental geometry and function in a 15-segment model. We have explored segmental differences between different ages and genders. Further studies are warranted to justify the importance of segmental assessment of the RV in different cardiac diseases. Abstract Figure. Separation of 15 standard RV segments

Fixed myocardial perfusion defects on SPECT are not associated with focal myocardial fibrosis on CMR in adult patients with systemic right ventricle

Abstract Funding Acknowledgements Type of funding sources: None. Background Myocardial fibrosis is a known prognostic factor in patients with systemic right ventricle (SRV). In these patients fixed myocardial perfusion defects are a common finding and are thought to represent areas of myocardial infarction and fibrosis. However, no study has yet correlated myocardial perfusion imaging findings with cardiac magnetic resonance (CMR) imaging, which is the imaging gold standard for detecting myocardial fibrosis. Purpose Our aim was to evaluate whether fixed myocardial perfusion defects in adult patients with SRV represent myocardial fibrosis. Methods Patients with SRV followed at our outpatient clinic for congenital heart disease were prospectively included. Myocardial perfusion was evaluated with a two-day stress/rest single-photon emission computed tomography (SPECT) protocol, focal myocardial fibrosis with late gadolinium enhancement (LGE) and diffuse myocardial fibrosis with T1 mapping by CMR. The 12-segment model of the right ventricle was used to report segments with myocardial perfusion defects and fibrosis (Figure 1). Results Fifteen patients with SRV (12 patients with transposition of the great arteries following atrial switch procedure and 3 patients with congenitally corrected transposition of the great arteries; 4 (26.7%) females; mean age 34.6 ± 10.0 years) were included. Myocardial perfusion defects were present in 14 patients (93%), with predominate fixed perfusion defects (73%) and less common reversible perfusion defects (27%). Fixed myocardial perfusion defects were most frequent in anterior RV segments (figure 1), with multiple segments affected in 11 patients (median number of affected segments – 2 segments). CMR was possible in 11 (73%) patients, others had a permanent pacemaker. LGE indicating focal myocardial fibrosis was detected in only 1 (9%) patient, while increased T1 values indicating diffuse myocardial fibrosis were present in 7 (64%) patients. There was no matching between areas of fixed myocardial perfusion defects and focal myocardial fibrosis in individual patients. Conclusions In our study, fixed myocardial perfusion defects detected on SPECT in patients with SRV did not represent areas of focal myocardial fibrosis on CMR. Other causes than scar may explain the frequently reported fixed perfusion defects, such as SRV anatomy with anterior position of the outflow tract and aorta, SRV morphology with variable degree of wall thickness and hypertrophy that influences tracer accumulation and image quality, or difficulties due to complex image acquisition and interpretation. To improve the diagnostic accuracy, the use of fused imaging modalities (SPECT-CT or PET-CT) is recommended in patients with SRV. Figure 1. Bull`s eye 12-segment plots of the right ventricle (RV) representing the number of segments with fixed myocardial perfusion defects detected by SPECT (1A) and LGE by CMR (1B) in patients with SRV. ANT – anterior, FW – free wall, INF – inferior, SEP – septal wall of RV. Abstract Figure.

Automatic Segmentation of Right Ventricle in Cine Cardiac Magnetic Resonance Image Based on a Dense and Multi-Scale U-net Method

Automatic Segmentation of Right Ventricle in Cine Cardiac Magnetic Resonance Image Based on a Dense and Multi-Scale U-net Method

Assessment of Right Ventricular Function Using Strain and Strain Rate in Patient with Acute Anterior ST Segment Elevation Myocardial Infarction

Introduction: Anterior ST-segment elevation myocardial infarctions (STEMI) are those involving the anterior ECG leads of the heart as a result of transmural ischaemia. They can be subdivided according to their extension into anteroseptal and anterolateral infarctions. Objective: The aim of this study is assessment of right ventricular function using strain and strain rate imaging in patients with anterior ST segment elevated myocardial infarction (STEMI). Methods: This prospective study was conducted on 35 patients with anterior ST segment elevated myocardial infarction (STEMI) who were admitted to ER and CCU department in National Heart Institute from May 2018 to May 2019. Results: Comparison between groups regarding longitudinal strain showed that there is highly statistically significant difference between them in lateral wall (all segments), septum (all segments) and global LS of right ventricle. Comparison between studied groups regarding circumferential strain revealed that there is highly statistically significant difference between them in all segments of right ventricle. Conclusions:Strain and strain rate imaging are feasible and reproducible tools in assessment of right ventricular function in patients with anterior ST segment elevated myocardial infarction (STEMI).

544 Deep learning in segmentation and function evaluation of right ventricle in 2D echocardiography

Abstract INTRODUCTION Deep learning (DL) has been of increasing use in the field of echocardiographic cardiology. The importance of segmentation and recognition of different heart chambers was already presented in different studies. However, there are no studies made regarding the functional heart measurements. Even though, functional measurements of right ventricle (RV) remains "dark side of the moon", no doubtfully severity of RV dysfunction influences the worse outcomes. PURPOSE To evaluate DL for recognition of geometrical features of RV and measurement of RV fractional area change (FAC). METHODS A total of 896 end-systolic and end-diastolic frames from 129 patients (with various indications for the study) were used to train and validate the neural networks. Raw pixel data was extracted from EPIQ 7G (Philips) imaging platform. All of the images were from 2D echocardiography apical four chamber views. RV was annotated in each image, with 1716 images used for training and 180 for validation. We used the state of art mask regional convolutional neural network (MR-CNN) and attention U-net convolutional neural network models for the RV segmentation task. Intersection over Union (IoU) was used as the primary metric for model evaluation. IoU measures the number of pixels common between the target and the prediction masks divided by the total number of pixels present across both masks. Additionally FAC was calculated using frames with minimal and maximal segmented area by the network. RESULTS U-Net architecture demonstrated considerably faster training compared to MR-CNN with time per training step of 85 ms and 750 ms for U-Net and MR-CNN, respectively (p < 0.001). MR-CNN and U-Net had an IoU of 0.91 and 0.89 respectively on validation dataset which corresponds to good performance of the model and there was no significant difference between the different neural networks (p = 0.876). Comparing the evaluation of FAC by physician and U-Net the mean squared difference was 12% when using minimum and maximum right ventricle area detected by the network. CONCLUSION With small dataset deep learning give us ability to recognize RV and measure RV FAC in apical four chamber view with high accuracy. This method offers assessment of RV to become daily used in the cardiologist practice, moreover, in the near future automated measurements will allow to reduce the need of observer manual evaluation. Improvements can be made in FAC calculation by also improving techniques for end-systolic and end-diastolic frame detection.

A new method of multi- atlas segmentation of right ventricle based on cardiac film magnetic resonance images

Segmentation of right ventricle (RV) in a cine cardiac magnetic resonance (CMR) image is essential for the diagnosis and therapy of cardiac diseases. Traditional image segmentation methods fail to achieve high accuracy due to the complex structure of RV. Multi-atlas frame, which transforms the segmentation into registration and fusion, has become one of the main segmentation methods of RV in recent years. In this paper, we suggest a new multi-atlas frame for the automatical and accurate segmentation of RV. Firstly, an adaptive affinity propagation algorithm is used to obtain a series of atlases, in which the atlas set most similar to the target image based on hausdorff distance and normalized mutual information is selected. Then, the target image is registered onto the selected atlas by using multi-resolution strategy-based affine transform and Diffeomorphic demons algorithm to generate a deformation field, which is applied to the label image to obtain coarse segmentation results of RV. Finally, the Consensus Level, Labeler Accuracy and Truth Estimation (COLLATE) algorithm is used to fuse the coarse segmentation result to obtain the RV. The 30 cine CMR datasets are applied to the retrospective analysis. The comparison between RV value from the present algorithm and that from the manual segmentation shows that the average dice index and hausdorff distance are 0.84 and 11.46 mm, respectively, the correlation coefficients and deviation means of endo-diastolic volume, endo-systolic volume and ejection fraction are 0.94, 0.90, 0.86, and 2.5113, –3.4783, 0.0341, respectively. Compared with convolutional neural networks, the new multi-atlas frame has an endo-systolic volume close to the manual result. The results show that the suggested method improves the accuracy and robustness of segmentation of RV from the effective atlas selection and multi-resolution Diffeomorphic demons algorithm-based registration, and it promises to be applied to clinical diagnosis.

Fully automatic segmentation of 4D MRI for cardiac functional measurements.

Segmentation of cardiac medical images, an important step in measuring cardiac function, is usually performed either manually or semiautomatically. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV) as well as the myocardium of three-dimensional (3D) magnetic resonance (MR) images throughout the entire cardiac cycle (four-dimensional, 4D), remains challenging. This study proposes a deformable-based segmentation methodology for efficiently segmenting 4D (3D+t) cardiac MR images. The proposed methodology first used the Hough transform and the local Gaussian distribution method (LGD) to segment the LV endocardial contours from cardiac MR images. Following this, a novel level set-based shape prior method was applied to generate the LV epicardial contours and the RV boundary. This automatic image segmentation approach has been applied to studies on 17 subjects. The results demonstrated that the proposed method was efficient compared to manual segmentation, achieving a segmentation accuracy with average Dice values of 88.62±5.47%, 87.35±7.26%, and 82.63±6.22% for the LV endocardial, LV epicardial, and RV contours, respectively. We have presented a method for accurate LV and RV segmentation. Compared to three existing methods, the proposed method can successfully segment the LV and yield the highest Dice value. This makes it an option for clinical assessment of the volume, size, and thickness of the ventricles.

Local Motion Intensity Clustering (LMIC) Model for Segmentation of Right Ventricle in Cardiac MRI Images.

Analysis of the morphology and function of the right ventricle (RV) can be used for the prediction and diagnosis of cardiovascular disease. Accurate description of the structure and function of heart can be provided by analyzing cardiac magnetic resonance imaging (MRI) images. Noise interference and intensity inhomogeneity of MRI images can be addressed by using a local intensity clustering (LIC) model. However, the segmentation of the RV in MRI images still remains a challenge mainly due to its ill-defined borders. To address such a challenge, an algorithm for segmenting the RV based on a local motion intensity clustering (LMIC) model is proposed in this paper. The LMIC model combines the LIC model with the motion intensity information, due to cardiac motion and blood flow. The motion intensity is calculated by using the Lucas Kanade optical flow method and utilized in the LMIC model as an energy parameter. Because the motion intensity of the RV region is stronger than other areas, the RV can be accurately segmented by this approach. Experimental results demonstrate that the LMIC model is able to address the challenge of the ill-defined RV borders in cardiac MRI images and improved RV segmentation accuracy over existing methods.

Correlated Regression Feature Learning for Automated Right Ventricle Segmentation.

Accurate segmentation of right ventricle (RV) from cardiac magnetic resonance (MR) images can help a doctor to robustly quantify the clinical indices including ejection fraction. In this paper, we develop one regression convolutional neural network (RegressionCNN) which combines a holistic regression model and a convolutional neural network (CNN) together to determine boundary points’ coordinates of RV directly and simultaneously. In our approach, we take the fully connected layers of CNN as the holistic regression model to perform RV segmentation, and the feature maps extracted by convolutional layers of CNN are converted into 1-D vector to connect holistic regression model. Such connection allows us to make full use of the optimization algorithm to constantly optimize the convolutional layers to directly learn the holistic regression model in the training process rather than separate feature extraction and regression model learning. Therefore, RegressionCNN can achieve optimally convolutional feature learning for accurately catching the regression features that are more correlated to RV regression segmentation task in training process, and this can reduce the latent mismatch influence between the feature extraction and the following regression model learning. We evaluate the performance of RegressionCNN on cardiac MR images acquired of 145 human subjects from two clinical centers. The results have shown that RegressionCNN’s results are highly correlated (average boundary correlation coefficient equals 0.9827) and consistent with the manual delineation (average dice metric equals 0.8351). Hence, RegressionCNN could be an effective way to segment RV from cardiac MR images accurately and automatically.