Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI

S-Net: a multiple cross aggregation convolutional architecture for automatic segmentation of small/thin structures for cardiovascular applications

With the success of U-Net or its variants in automatic medical image segmentation, building a fully convolutional network (FCN) based on an encoder-decoder structure has become an effective end-to-end learning approach. However, the intrinsic property of FCNs is that as the encoder deepens, higher-level features are learned, and the receptive field size of the network increases, which results in unsatisfactory performance for detecting low-level small/thin structures such as atrial walls and small arteries. To address this issue, we propose to keep the different encoding layer features at their original sizes to constrain the receptive field from increasing as the network goes deeper. Accordingly, we develop a novel S-shaped multiple cross-aggregation segmentation architecture named S-Net, which has two branches in the encoding stage, i.e., a resampling branch to capture low-level fine-grained details and thin/small structures and a downsampling branch to learn high-level discriminative knowledge. In particular, these two branches learn complementary features by residual cross-aggregation; the fusion of the complementary features from different decoding layers can be effectively accomplished through lateral connections. Meanwhile, we perform supervised prediction at all decoding layers to incorporate coarse-level features with high semantic meaning and fine-level features with high localization capability to detect multi-scale structures, especially for small/thin volumes fully. To validate the effectiveness of our S-Net, we conducted extensive experiments on the segmentation of cardiac wall and intracranial aneurysm (IA) vasculature, and quantitative and qualitative evaluations demonstrated the superior performance of our method for predicting small/thin structures in medical images.

Automatic right ventricular segmentation for cine cardiac magnetic resonance images based on a new deep atlas network

BACKGROUND

The high morbidity and mortality of heart disease present a significant threat to human health. The development of methods for the quick and accurate diagnosis of heart diseases, enabling their effective treatment, has become a key issue of concern. Right ventricular (RV) segmentation from cine cardiac magnetic resonance (CMR) images plays a significant role in evaluating cardiac function for clinical diagnosis and prognosis. However, due to the complex structure of the RV, traditional segmentation methods are ineffective for RV segmentation.

PURPOSE

In this paper, we propose a new deep atlas network to improve the learning efficiency and segmentation accuracy of a deep learning network by integrating multi-atlas.

METHODS

First, a dense multi-scale U-net (DMU-net) is presented to acquire transformation parameters from atlas images to target images. The transformation parameters map the atlas image labels to the target image labels. Second, using a spatial transformation layer, the atlas images are deformed based on these parameters. Finally, the network is optimized by backpropagation with two loss functions where the mean squared error function (MSE) is used to measure the similarity of the input images and transformed images. Further, the Dice metric (DM) is used to quantify the overlap between the predicted contours and the ground truth. In our experiments, 15 datasets are used in testing, and 20 cine CMR images are selected as atlas.

RESULTS

The mean values and standard deviations for the DM and Hausdorff distance are 0.871 and 4.67 mm, 0.104 and 2.528 mm, respectively. The correlation coefficients of endo-diastolic volume, endo-systolic volume, ejection fraction, and stroke volume are 0.984, 0.926, 0.980, and 0.991, respectively, and the mean differences between all of the mentioned parameters are 3.2, -1.7, 0.02, and 4.9, respectively. Most of these differences are within the allowable range of 95%, indicating that the results are acceptable and show good consistency. The segmentation results obtained in this method are compared with those obtained by other methods that provide satisfactory performance. The other methods provide better segmentation effects at the base, but either no segmentation or the wrong segmentation at the top, which demonstrate that the deep atlas network can improve top-area segmentation accuracy.

CONCLUSION

Our results indicate that the proposed method can achieve better segmentation results than the previous methods, with both high relevance and consistency, and has the potential for clinical application.

A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images

Accurate segmentation of the left ventricle (LV) is crucial for evaluating myocardial perfusion SPECT (MPS) and assessing LV functions. In this study, a novel method combining deep learning with shape priors was developed and validated to extract the LV myocardium and automatically measure LV functional parameters. The method integrates a three-dimensional (3D) V-Net with a shape deformation module that incorporates shape priors generated by a dynamic programming (DP) algorithm to guide its output during training. A retrospective analysis was performed on an MPS dataset comprising 31 subjects without or with mild ischemia, 32 subjects with moderate ischemia, and 12 subjects with severe ischemia. Myocardial contours were manually annotated as the ground truth. A 5-fold stratified cross-validation was used to train and validate the models. The clinical performance was evaluated by measuring LV end-systolic volume (ESV), end-diastolic volume (EDV), left ventricular ejection fraction (LVEF), and scar burden from the extracted myocardial contours. There were excellent agreements between segmentation results by our proposed model and those from the ground truth, with a Dice similarity coefficient (DSC) of 0.9573 ± 0.0244, 0.9821 ± 0.0137, and 0.9903 ± 0.0041, as well as Hausdorff distances (HD) of 6.7529 ± 2.7334 mm, 7.2507 ± 3.1952 mm, and 7.6121 ± 3.0134 mm in extracting the LV endocardium, myocardium, and epicardium, respectively. Furthermore, the correlation coefficients between LVEF, ESV, EDV, stress scar burden, and rest scar burden measured from our model results and the ground truth were 0.92, 0.958, 0.952, 0.972, and 0.958, respectively. The proposed method achieved a high accuracy in extracting LV myocardial contours and assessing LV functions.

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.

Bi-ventricular assessment with cardiovascular magnetic resonance at 5 Tesla: A pilot study

Background

Cardiovascular magnetic resonance (CMR) imaging at ultra-high fields (UHF) such as 7T has encountered many challenges such as faster T2* relaxation, stronger B₀ and B₁₊ field inhomogeneities and additional safety concerns due to increased specific absorption rate (SAR) and peripheral nervous stimulation (PNS). Recently, a new line of 5T whole body MRI system has become available, and this study aims at evaluating the performance and benefits of this new UHF system for CMR imaging.

Methods

Gradient echo (GRE) CINE imaging was performed on healthy volunteers at both 5 and 3T, and was compared to balanced steady-state-free-procession (bSSFP) CINE imaging at 3T as reference. Higher spatial resolution GRE CINE scans were additionally performed at 5T. All scans at both fields were performed with ECG-gating and breath-holding. Image quality was blindly evaluated by two radiologists, and the cardiac functional parameters (e.g., EDV/ESV/mass/EF) of the left and right ventricles were measured for statistical analyses using the Wilcoxon signed-rank test and Bland-Altman analysis.

Results

Compared to 3T GRE CINE imaging, 5T GRE CINE imaging achieved comparable or improved image quality with significantly superior SNR and CNR, and it has also demonstrated excellent capability for high resolution (1.0 × 1.0 × 6.0 mm³) imaging. Functional assessments from 5T GRE CINE images were highly similar with the 3T bSSFP CINE reference.

Conclusions

This pilot study has presented the initial evaluation of CMR CINE imaging at 5T UHF, which yielded superior image quality and accurate functional quantification when compared to 3T counterparts. Along with reliable ECG gating, the new 5T UHF system has the potential to achieve well-balanced performance for CMR applications.

Computational Modeling of Right Ventricular Motion and Intracardiac Flow in Repaired Tetralogy of Fallot

PURPOSE

Patients with repaired Tetralogy of Fallot (rTOF) will develop dilation of the right ventricle (RV) from chronic pulmonary insufficiency and require pulmonary valve replacement (PVR). Cardiac MRI (cMRI) is used to guide therapy but has limitations in studying novel intracardiac flow parameters. This pilot study aimed to demonstrate feasibility of reconstructing RV motion and simulating intracardiac flow in rTOF patients, exclusively using conventional cMRI and an immersed-boundary method computational fluid dynamic (CFD) solver.

METHODS

Four rTOF patients and three normal controls underwent cMRI including 4D flow. 3D RV models were segmented from cMRI images. Feature-tracking software captured RV endocardial contours from cMRI long-axis and short-axis cine stacks. RV motion was reconstructed via diffeomorphic mapping (Deformetrica, deformetrica.org), serving as the domain boundary for CFD. Fully-resolved direct numerical simulations were performed over several cardiac cycles. Intracardiac vorticity, kinetic energy (KE) and turbulent kinetic energy (TKE) was measured. For validation, RV motion was compared to manual tracings, results of KE were compared between CFD and 4D flow.

RESULTS

Diastolic vorticity and TKE in rTOF patients were 4.12 ± 2.42 mJ/L and 115 ± 27/s, compared to 2.96 ± 2.16 mJ/L and 78 ± 45/s in controls. There was good agreement between RV motion and manual tracings. The difference in diastolic KE between CFD and 4D flow by Bland-Altman analysis was - 0.89910 to 2 mJ/mL (95% limits of agreement: - 1.351 × 10^-2 mJ/mL to 1.171 × 10^-2 mJ/mL).

CONCLUSION

This CFD framework can produce intracardiac flow in rTOF patients. CFD has the potential for predicting the effects of PVR in rTOF patients and improve the clinical indications guided by cMRI.

Medical image segmentation with generative adversarial semi-supervised network

Recent medical image segmentation methods heavily rely on large-scale training data and high-quality annotations. However, these resources are hard to obtain due to the limitation of medical images and professional annotators. How to utilize limited annotations and maintain the performance is an essential yet challenging problem. In this paper, we try to tackle this problem in a self-learning manner by proposing a generative adversarial semi-supervised network. We use limited annotated images as main supervision signals, and the unlabeled images are manipulated as extra auxiliary information to improve the performance. More specifically, we modulate a segmentation network as a generator to produce pseudo labels for unlabeled images. To make the generator robust, we train an uncertainty discriminator with generative adversarial learning to determine the reliability of the pseudo labels. To further ensure dependability, we apply feature mapping loss to obtain statistic distribution consistency between the generated labels and the real labels. Then the verified pseudo labels are used to optimize the generator in a self-learning manner. We validate the effectiveness of the proposed method on right ventricle dataset, Sunnybrook dataset, STACOM, ISIC dataset, and Kaggle lung dataset. We obtain 0.8402-0.9121, 0.8103-0.9094, 0.9435-0.9724, 0.8635-0.886, and 0.9697-0.9885 dice coefficient with 1/8 to 1/2 proportion of densely annotated labels, respectively. The improvements are up to 28.6 points higher than the corresponding fully supervised baseline.

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.

Multiple two-dimensional active shape model framework for right ventricular segmentation

Segmentation of the right ventricle (RV) in MRI short axis images is very challenging due to its complex shape and various appearance among the different subjects and cross-sections. Active shape models (ASM) have shown potential for segmenting the complex structures, including the RV, through two formulations: two- and three-dimensional modeling with a reported trade-off between accuracy and complexity of each formulation. In this work, we propose a new framework for modeling the RV surface using multiple 2D contours, where information from multiple cross-sectional images are incorporated into the same model. The proposed method was tested using cardiac MRI images from 56 human subjects. Compared to a golden reference of manually delineated RV contours, the proposed method resulted in significantly lower error than (almost one half) that of the conventional 2D ASM especially at the apical slices. The mean absolute distance of the proposed method was 2.9 ± 2 mm while the conventional 2D ASM resulted in an error of 6.6 ± 4.5 mm. In addition, the computation time of the proposed method was 5 s compared to 4 ± 1 min previously reported for the 3D ASM formulation.

TSU-net: Two-stage multi-scale cascade and multi-field fusion U-net for right ventricular segmentation

Accurate segmentation of the right ventricle from cardiac magnetic resonance images (MRI) is a critical step in cardiac function analysis and disease diagnosis. It is still an open problem due to some difficulties, such as a large variety of object sizes and ill-defined borders. In this paper, we present a TSU-net network that grips deeper features and captures targets of different sizes with multi-scale cascade and multi-field fusion in the right ventricle. TSU-net mainly contains two major components: Dilated-Convolution Block (DB) and Multi-Layer-Pool Block (MB). DB extracts and aggregates multi-scale features for the right ventricle. MB mainly relies on multiple effective field-of-views to detect objects at different sizes and fill boundary features. Different from previous networks, we used DB and MB to replace the convolution layer in the encoding layer, thus, we can gather multi-scale information of right ventricle, detect different size targets and fill boundary information in each encoding layer. In addition, in the decoding layer, we used DB to replace the convolution layer, so that we can aggregate the multi-scale features of the right ventricle in each decoding layer. Furthermore, the two-stage U-net structure is used to further improve the utilization of DB and MB through a two-layer encoding/decoding layer. Our method is validated on the RVSC, a public right ventricular data set. The results demonstrated that TSU-net achieved an average Dice coefficient of 0.86 on endocardium and 0.90 on the epicardium, thereby outperforming other models. It effectively assists doctors to diagnose the disease and promotes the development of medical images. In addition, we also provide an intuitive explanation of our network, which fully explain MB and TSU-net's ability to detect targets of different sizes and fill in boundary features.

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.

Clinical Performance and Role of Expert Supervision of Deep Learning for Cardiac Ventricular Volumetry: A Validation Study

Purpose

To evaluate the performance of a deep learning (DL) algorithm for clinical measurement of right and left ventricular volume and function across cardiac MR images obtained for a range of clinical indications and pathologies.

Materials and Methods

A retrospective, Health Insurance Portability and Accountability Act-compliant study was conducted using the first 200 noncongenital clinical cardiac MRI examinations from June 2015 to June 2017 for which volumetry was available. Images were analyzed using commercially available software for automated DL-based and manual contouring of biventricular volumes. Fully automated measurements were compared using Pearson correlations, relative volume errors, and Bland-Altman analyses. Manual, automated, and expert revised contours for 50 MR images were examined by comparing regional Dice coefficients at the base, midventricle, and apex to further analyze the contour quality.

Results

Fully automated and manual left ventricular volumes were strongly correlated for end-systolic volume (ESV: Pearson r = 0.99, P < .001), end-diastolic volume (EDV: r = 0.97, P < .001), and ejection fraction (EF: r = 0.94, P < .001). Right ventricular measurements were also correlated for ESV (r = 0.93, P < .001), EDV (r = 0.92, P < .001), and EF (r = 0.73, P < .001). Visual inspection of segmentation quality showed most errors (73%) occurred at the cardiac base. Mean Dice coefficients between manual, automated, and expert revised contours ranged from 0.92 to 0.95, with greatest variance at the base and apex.

Conclusion

Fully automated ventricular segmentation by the tested algorithm provides contours and ventricular volumes that could be used to aid expert segmentation, but can benefit from expert supervision, particularly to resolve errors at the basal and apical slices. Supplemental material is available for this article. © RSNA, 2020.

A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images

Cardiac MRI has been widely used for noninvasive assessment of cardiac anatomy and function as well as heart diagnosis. The estimation of physiological heart parameters for heart diagnosis essentially require accurate segmentation of the Left ventricle (LV) from cardiac MRI. Therefore, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aim to achieve lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Our framework starts by an accurate localization of the LV blood pool center-point using a fully convolutional neural network (FCN) architecture called FCN1. Then, a region of interest (ROI) that contains the LV is extracted from all heart sections. The extracted ROIs are used for the segmentation of LV cavity and myocardium via a novel FCN architecture called FCN2. The FCN2 network has several bottleneck layers and uses less memory footprint than conventional architectures such as U-net. Furthermore, a new loss function called radial loss that minimizes the distance between the predicted and true contours of the LV is introduced into our model. Following myocardial segmentation, functional and mass parameters of the LV are estimated. Automated Cardiac Diagnosis Challenge (ACDC-2017) dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. To sum up, we propose a deep learning approach that can be translated into a clinical tool for heart diagnosis.

Evaluation of a Semi-automatic Right Ventricle Segmentation Method on Short-Axis MR Images

The purpose of this study was to evaluate a semi-automatic right ventricle segmentation method on short-axis cardiac cine MR images which segment all right ventricle contours in a cardiac phase using one seed contour. Twenty-eight consecutive short-axis, four-chamber, and tricuspid valve view cardiac cine MRI examinations of healthy volunteers were used. Two independent observers performed the manual and automatic segmentations of the right ventricles. Analyses were based on the ventricular volume and ejection fraction of the right heart chamber. Reproducibility of the manual and semi-automatic segmentations was assessed using intra- and inter-observer variability. Validity of the semi-automatic segmentations was analyzed with reference to the manual segmentations. The inter- and intra-observer variability of manual segmentations were between 0.8 and 3.2%. The semi-automatic segmentations were highly correlated with the manual segmentations (R² 0.79-0.98), with median difference of 0.9-4.8% and of 3.3% for volume and ejection fraction parameters, respectively. In comparison to the manual segmentation, the semi-automatic segmentation produced contours with median dice metrics of 0.95 and 0.87 and median Hausdorff distance of 5.05 and 7.35 mm for contours at end-diastolic and end-systolic phases, respectively. The inter- and intra-observer variability of the semi-automatic segmentations were lower than observed in the manual segmentations. Both manual and semi-automatic segmentations performed better at the end-diastolic phase than at the end-systolic phase. The investigated semi-automatic segmentation method managed to produce a valid and reproducible alternative to manual right ventricle segmentation.

Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging

Advances in technology have always had the potential and opportunity to shape the practice of medicine, and in no medical specialty has technology been more rapidly embraced and adopted than radiology. Machine learning and deep neural networks promise to transform the practice of medicine, and, in particular, the practice of diagnostic radiology. These technologies are evolving at a rapid pace due to innovations in computational hardware and novel neural network architectures. Several cutting-edge postprocessing analysis applications are actively being developed in the fields of thoracic and cardiovascular imaging, including applications for lesion detection and characterization, lung parenchymal characterization, coronary artery assessment, cardiac volumetry and function, and anatomic localization. Cardiothoracic and cardiovascular imaging lies at the technological forefront of radiology due to a confluence of technical advances. Enhanced equipment has enabled computed tomography and magnetic resonance imaging scanners that can safely capture images that freeze the motion of the heart to exquisitely delineate fine anatomic structures. Computing hardware developments have enabled an explosion in computational capabilities and in data storage. Progress in software and fluid mechanical models is enabling complex 3D and 4D reconstructions to not only visualize and assess the dynamic motion of the heart, but also quantify its blood flow and hemodynamics. And now, innovations in machine learning, particularly in the form of deep neural networks, are enabling us to leverage the increasingly massive data repositories that are prevalent in the field. Here, we discuss developments in machine learning techniques and deep neural networks to highlight their likely role in future radiologic practice, both in and outside of image interpretation and analysis. We discuss the concepts of validation, generalizability, and clinical utility, as they pertain to this and other new technologies, and we reflect upon the opportunities and challenges of bringing these into daily use.

Dilated-Inception Net: Multi-Scale Feature Aggregation for Cardiac Right Ventricle Segmentation

Segmentation of cardiac ventricle from magnetic resonance images is significant for cardiac disease diagnosis, progression assessment, and monitoring cardiac conditions. Manual segmentation is so time consuming, tedious, and subjective that automated segmentation methods are highly desired in practice. However, conventional segmentation methods performed poorly in cardiac ventricle, especially in the right ventricle. Compared with the left ventricle, whose shape is a simple thick-walled circle, the structure of the right ventricle is more complex due to ambiguous boundary, irregular cavity, and variable crescent shape. Hence, effective feature extractors and segmentation models are preferred. In this paper, we propose a dilated-inception net (DIN) to extract and aggregate multi-scale features for right ventricle segmentation. The DIN outperforms many state-of-the-art models on the benchmark database of right ventricle segmentation challenge. In addition, the experimental results indicate that the proposed model has potential to reach expert-level performance in right ventricular epicardium segmentation. More importantly, DIN behaves similarly to clinical expert with high correlation coefficients in four clinical cardiac indices. Therefore, the proposed DIN is promising for automated cardiac right ventricle segmentation in clinical applications.

Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation

In this paper, we present a novel convolutional neural network architecture to segment images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed model is an extension of the U-net that embeds a cardiac shape prior and involves a loss function tailored to the cardiac anatomy. Since the shape prior is computed offline only once, the execution of our model is not limited by its calculation. Our system takes as input raw magnetic resonance images, requires no manual preprocessing or image cropping and is trained to segment the endocardium and epicardium of the left ventricle, the endocardium of the right ventricle, as well as the center of the left ventricle. With its multiresolution grid architecture, the network learns both high and low-level features useful to register the shape prior as well as accurately localize the borders of the cardiac regions. Experimental results obtained on the Automatic Cardiac Diagnostic Challenge - Medical Image Computing and Computer Assisted Intervention (ACDC-MICCAI) 2017 dataset show that our model segments multislices CMRI (left and right ventricle contours) in 0.18 s with an average Dice coefficient of [Formula: see text] and an average 3-D Hausdorff distance of [Formula: see text] mm.

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.

Segmentation of Left and Right Ventricles in Cardiac MRI Using Active Contours

Segmentation of left and right ventricles plays a crucial role in quantitatively analyzing the global and regional information in the cardiac magnetic resonance imaging (MRI). In MRI, the intensity inhomogeneity and weak or blurred object boundaries are the problems, which makes it difficult for the intensity-based segmentation methods to properly delineate the regions of interests (ROI). In this paper, a hybrid signed pressure force function (SPF) is proposed, which yields both local and global image fitted differences in an additive fashion. A characteristic term is also introduced in the SPF function to restrict the contour within the ROI. The overlapping dice index and Hausdorff-Distance metrics have been used over cardiac datasets for quantitative validation. Using 2009 LV MICCAI validation dataset, the proposed method yields DSC values of 0.95 and 0.97 for endocardial and epicardial contours, respectively. Using 2012 RV MICCAI dataset, for the endocardial region, the proposed method yields DSC values of 0.97 and 0.90 and HD values of 8.51 and 7.67 for ED and ES, respectively. For the epicardial region, it yields DSC values of 0.92 and 0.91 and HD values of 6.47 and 9.34 for ED and ES, respectively. Results show its robustness in the segmentation application of the cardiac MRI.

Automatic regional analysis of myocardial native T1 values: left ventricle segmentation and AHA parcellations

Native T1 value is emerging as a reliable indicator of abnormal heart conditions related to myocardial fibrosis. Investigators have extensively used the standardized myocardial segmentation of the American Heart Association (AHA) to measure regional T1 values of the left ventricular (LV) walls. In this paper, we present a fully automatic system to analyze modified Look-Locker inversion recovery images and to report regional T1 values of AHA segments. Ten healthy individuals participated in the T1 mapping study with a 3.0 T scanner after providing informed consent. First, we obtained masks of an LV blood-pool region and LV walls by using an image synthesis method and a layer-growing method. Subsequently, the LV walls were divided into AHA segments by identifying the boundaries of the septal regions and by using a radial projection method. The layer-growing method significantly enhanced the accuracy of the derived myocardium mask. We compared the T1 values that were obtained using manual region of interest selections and those obtained using the automatic system. The average T1 difference of the calculated segments was 4.6 ± 1.5%. This study demonstrated a practical and robust method of obtaining native T1 values of AHA segments in LV walls.

Automatic segmentation of right ventricle in cardiac cine MR images using a saliency analysis

PURPOSE

Accurate measurement of the right ventricle (RV) volume is important for the assessment of the ventricular function and a biomarker of the progression of any cardiovascular disease. However, the high RV variability makes difficult a proper delineation of the myocardium wall. This paper introduces a new automatic method for segmenting the RV volume from short axis cardiac magnetic resonance (MR) images by a salient analysis of temporal and spatial observations.

METHODS

The RV volume estimation starts by localizing the heart as the region with the most coherent motion during the cardiac cycle. Afterward, the ventricular chambers are identified at the basal level using the isodata algorithm, the right ventricle extracted, and its centroid computed. A series of radial intensity profiles, traced from this centroid, is used to search a salient intensity pattern that models the inner-outer myocardium boundary. This process is iteratively applied toward the apex, using the segmentation of the previous slice as a regularizer. The consecutive 2D segmentations are added together to obtain the final RV endocardium volume that serves to estimate also the epicardium.

RESULTS

Experiments performed with a public dataset, provided by the RV segmentation challenge in cardiac MRI, demonstrated that this method is highly competitive with respect to the state of the art, obtaining a Dice score of 0.87, and a Hausdorff distance of 7.26 mm while a whole volume was segmented in about 3 s.

CONCLUSIONS

The proposed method provides an useful delineation of the RV shape using only the spatial and temporal information of the cine MR images. This methodology may be used by the expert to achieve cardiac indicators of the right ventricle function.

Unsupervised Freeview Groupwise Cardiac Segmentation Using Synchronized Spectral Network

The diagnosis, comparative and population study of cardiac radiology data require heart segmentation on increasingly large amount of images from different modalities/chambers/patients under various imaging views. Most existing automatic cardiac segmentation methods are often limited to single image segmentation with regulated modality/region settings or well-cropped ROI areas, which is impossible for large datasets due to enormous device protocols and institutional differences. A pure data-driven unsupervised segmentation without regulated setting requirements is crucial in this scenario, and will significantly automate the manual work and adopt the various changes of modality, subject or view. In this paper, we propose a general unsupervised groupwise segmentation: a direct simultaneous segmentation for a group of multi-modality, multi-chamber, multi-subject ( M³) cardiac images from a freely chosen imaging view. The segmentation can directly perform not only on regulated two/four-chamber images, but also on non-regulated uncropped raw MR/CT scans. A new Synchronized Spectral Network (SSN) is developed for the simultaneous decomposing, synchronizing, and clustering the spectral features of free-view M³ cardiac images. The SSN-based groupwise analysis of image spectral bases immediately leads to groupwise segmentation of M³ freeview images. The segmentation is quantitatively evaluated by three datasets (MR and CT mixed) with more than 200 subjects. High dice metric ( ) is consistently achieved in validation. Our method provides a powerful tool for medical images under general imaging environment.

A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging

Cardiovascular magnetic resonance (CMR) has become a key imaging modality in clinical cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac chambers and great vessels. A wide range of CMR sequences have been developed to assess various aspects of cardiac structure and function, and significant advances have also been made in terms of imaging quality and acquisition times. A lot of research has been dedicated to the development of global and regional quantitative CMR indices that help the distinction between health and pathology. The goal of this review paper is to discuss the structural and functional CMR indices that have been proposed thus far for clinical assessment of the cardiac chambers. We include indices definitions, the requirements for the calculations, exemplar applications in cardiovascular diseases, and the corresponding normal ranges. Furthermore, we review the most recent state-of-the art techniques for the automatic segmentation of the cardiac boundaries, which are necessary for the calculation of the CMR indices. Finally, we provide a detailed discussion of the existing literature and of the future challenges that need to be addressed to enable a more robust and comprehensive assessment of the cardiac chambers in clinical practice.

Distance regularized two level sets for segmentation of left and right ventricles from cine-MRI

This paper presents a new level set method for segmentation of cardiac left and right ventricles. We extend the edge based distance regularized level set evolution (DRLSE) model in Li et al. (2010) to a two-level-set formulation, with the 0-level set and k-level set representing the endocardium and epicardium, respectively. The extraction of endocardium and epicardium is obtained as a result of the interactive curve evolution of the 0 and k level sets derived from the proposed variational level set formulation. The initialization of the level set function in the proposed two-level-set DRLSE model is generated from roughly located endocardium, which can be performed by applying the original DRLSE model. Experimental results have demonstrated the effectiveness of the proposed two-level-set DRLSE model.

Right ventricular segmentation in cardiac MRI with moving mesh correspondences

This study investigates automatic propagation of the right ventricle (RV) endocardial and epicardial boundaries in 4D (3D+time) magnetic resonance imaging (MRI) sequences. Based on a moving mesh (or grid generation) framework, the proposed algorithm detects the endocardium and epicardium within each cardiac phase via point-to-point correspondences. The proposed method has the following advantages over prior RV segmentation works: (1) it removes the need for a time-consuming, manually built training set; (2) it does not make prior assumptions as to the intensity distributions or shape; (3) it provides a sequence of corresponding points over time, a comprehensive input that can be very useful in cardiac applications other than segmentation, e.g., regional wall motion analysis; and (4) it is more flexible for congenital heart disease where the RV undergoes high variations in shape. Furthermore, the proposed method allows comprehensive RV volumetric analysis over the complete cardiac cycle as well as automatic detections of end-systolic and end-diastolic phases because it provides a segmentation for each time step. Evaluated quantitatively over the 48-subject data set of the MICCAI 2012 RV segmentation challenge, the proposed method yielded an average Dice score of 0.84±0.11 for the epicardium and 0.79±0.17 for the endocardium. Further, quantitative evaluations of the proposed approach in comparisons to manual contours over 23 infant hypoplastic left heart syndrome patients yielded a Dice score of 0.82±0.14, which demonstrates the robustness of the algorithm.

Effects of fibrosis morphology on reentrant ventricular tachycardia inducibility and simulation fidelity in patient-derived models

Myocardial fibrosis detected via delayed-enhanced magnetic resonance imaging (MRI) has been shown to be a strong indicator for ventricular tachycardia (VT) inducibility. However, little is known regarding how inducibility is affected by the details of the fibrosis extent, morphology, and border zone configuration. The objective of this article is to systematically study the arrhythmogenic effects of fibrosis geometry and extent, specifically on VT inducibility and maintenance. We present a set of methods for constructing patient-specific computational models of human ventricles using in vivo MRI data for patients suffering from hypertension, hypercholesterolemia, and chronic myocardial infarction. Additional synthesized models with morphologically varied extents of fibrosis and gray zone (GZ) distribution were derived to study the alterations in the arrhythmia induction and reentry patterns. Detailed electrophysiological simulations demonstrated that (1) VT morphology was highly dependent on the extent of fibrosis, which acts as a structural substrate, (2) reentry tended to be anchored to the fibrosis edges and showed transmural conduction of activations through narrow channels formed within fibrosis, and (3) increasing the extent of GZ within fibrosis tended to destabilize the structural reentry sites and aggravate the VT as compared to fibrotic regions of the same size and shape but with lower or no GZ. The approach and findings represent a significant step toward patient-specific cardiac modeling as a reliable tool for VT prediction and management of the patient. Sensitivities to approximation nuances in the modeling of structural pathology by image-based reconstruction techniques are also implicated.