Max-flow segmentation of the left ventricle by recovering subject-specific distributions via a bound of the Bhattacharyya measure

Anatomical Prior-Based Automatic Segmentation for Cardiac Substructures from Computed Tomography Images

Cardiac substructure segmentation is a prerequisite for cardiac diagnosis and treatment, providing a basis for accurate calculation, modeling, and analysis of the entire cardiac structure. CT (computed tomography) imaging can be used for a noninvasive qualitative and quantitative evaluation of the cardiac anatomy and function. Cardiac substructures have diverse grayscales, fuzzy boundaries, irregular shapes, and variable locations. We designed a deep learning-based framework to improve the accuracy of the automatic segmentation of cardiac substructures. This framework integrates cardiac anatomical knowledge; it uses prior knowledge of the location, shape, and scale of cardiac substructures and separately processes the structures of different scales. Through two successive segmentation steps with a coarse-to-fine cascaded network, the more easily segmented substructures were coarsely segmented first; then, the more difficult substructures were finely segmented. The coarse segmentation result was used as prior information and combined with the original image as the input for the model. Anatomical knowledge of the large-scale substructures was embedded into the fine segmentation network to guide and train the small-scale substructures, achieving efficient and accurate segmentation of ten cardiac substructures. Sixty cardiac CT images and ten substructures manually delineated by experienced radiologists were retrospectively collected; the model was evaluated using the DSC (Dice similarity coefficient), Recall, Precision, and the Hausdorff distance. Compared with current mainstream segmentation models, our approach demonstrated significantly higher segmentation accuracy, with accurate segmentation of ten substructures of different shapes and sizes, indicating that the segmentation framework fused with prior anatomical knowledge has superior segmentation performance and can better segment small targets in multi-target segmentation tasks.

A task-unified network with transformer and spatial-temporal convolution for left ventricular quantification

Quantification of the cardiac function is vital for diagnosing and curing the cardiovascular diseases. Left ventricular function measurement is the most commonly used measure to evaluate the function of cardiac in clinical practice, how to improve the accuracy of left ventricular quantitative assessment results has always been the subject of research by medical researchers. Although considerable efforts have been put forward to measure the left ventricle (LV) automatically using deep learning methods, the accurate quantification is yet a challenge work as a result of the changeable anatomy structure of heart in the systolic diastolic cycle. Besides, most methods used direct regression method which lacks of visual based analysis. In this work, a deep learning segmentation and regression task-unified network with transformer and spatial-temporal convolution is proposed to segment and quantify the LV simultaneously. The segmentation module leverages a U-Net like 3D Transformer model to predict the contour of three anatomy structures, while the regression module learns spatial-temporal representations from the original images and the reconstruct feature map from segmentation path to estimate the finally desired quantification metrics. Furthermore, we employ a joint task loss function to train the two module networks. Our framework is evaluated on the MICCAI 2017 Left Ventricle Full Quantification Challenge dataset. The results of experiments demonstrate the effectiveness of our framework, which achieves competitive cardiac quantification metric results and at the same time produces visualized segmentation results that are conducive to later analysis.

Left ventricle segmentation combining deep learning and deformable models with anatomical constraints

Segmentation of the left ventricle is a key approach in Cardiac Magnetic Resonance Imaging for calculating biomarkers in diagnosis. Since there is substantial effort required from the expert, many automatic segmentation methods have been proposed, in which deep learning networks have obtained remarkable performance. However, one of the main limitations of these approaches is the production of segmentations that contain anatomical errors. To avoid this limitation, we propose a new fully-automatic left ventricle segmentation method combining deep learning and deformable models. We propose a new level set energy formulation that includes exam-specific information estimated from the deep learning segmentation and shape constraints. The method is part of a pipeline containing pre-processing steps and a failure correction post-processing step. Experiments were conducted with the Sunnybrook and ACDC public datasets, and a private dataset. Results suggest that the method is competitive, that it can produce anatomically consistent segmentations, has good generalization ability, and is often able to estimate biomarkers close to the expert.

Distilling sub-space structure across views for cardiac indices estimation

Cardiac indices estimation in multi-view images attracts great attention due to its capability for cardiac function assessment. However, the variation of the cardiac indices across views causes that most cardiac indices estimation methods can only be trained separately in each view, resulting in low data utilization. To solve this problem, we have proposed distilling the sub-space structure across views to explore the multi-view data fully for cardiac indices estimation. In particular, the sub-space structure is obtained via building a n×n covariance matrix to describe the correlation between the output dimensions of all views. Then, an alternate convex search algorithm is proposed to optimize the cross-view learning framework by which: (i) we train the model with regularization of sub-space structure in each view; (ii) we update the sub-space structure based on the learned parameters from all views. In the end, we have conducted a series of experiments to verify the effectiveness of our proposed framework. The model is trained on three views (short axis, 2-chamber view and 4-chamber view) with two modalities (magnetic resonance imaging and computed tomography). Compared to the state-of-the-art methods, our method has demonstrated superior performance on cardiac indices estimation tasks.

Ensemble of 2D Residual Neural Networks Integrated with Atrous Spatial Pyramid Pooling Module for Myocardium Segmentation of Left Ventricle Cardiac MRI

Cardiac disease diagnosis and identification is problematic mostly by inaccurate segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging since it involves complex and variable cardiac structures in terms of components and the intricacy of time-based crescendos. In addition, full segmentation and quantification of the LV myocardium border is even more challenging because of different shapes and sizes of the myocardium border zone. The foremost purpose of this research is to design a precise automatic segmentation technique employing deep learning models for the myocardium border using cardiac magnetic resonance imaging (MRI). The ASPP module (Atrous Spatial Pyramid Pooling) was integrated with a proposed 2D-residual neural network for segmentation of the myocardium border using a cardiac MRI dataset. Further, the ensemble technique based on a majority voting ensemble method was used to blend the results of recent deep learning models on different set of hyperparameters. The proposed model produced an 85.43% dice score on validation samples and 98.23% on training samples and provided excellent performance compared to recent deep learning models. The myocardium border was successfully segmented across diverse subject slices with different shapes, sizes and contrast using the proposed deep learning ensemble models. The proposed model can be employed for automatic detection and segmentation of the myocardium border for precise quantification of reflow, myocardial infarction, myocarditis, and h cardiomyopathy (HCM) for clinical applications.

Sequential shape similarity for active contour based left ventricle segmentation in cardiac cine MR image

Delineation of the boundaries of the Left Ventricle (LV) in cardiac Magnetic Resonance Images (MRI) is a hot topic due to its important diagnostic power. In this paper, an approach is proposed to extract the LV in a sequence of MR images. In the proposed paper, all images in the sequence are segmented simultaneously and the shape of the LV in each image is supposed to be similar to that of the LV in nearby images in the sequence. We coined the novel shape similarity constraint, and it is called sequential shape similarity (SSS in short). The proposed segmentation method takes the Active Contour Model as the base model and our previously proposed Gradient Vector Convolution (GVC) external force is also adopted. With the SSS constraint, the snake contour can accurately delineate the LV boundaries. We evaluate our method on two cardiac MRI datasets and the Mean Absolute Distance (MAD) metric and the Hausdorff Distance (HD) metric demonstrate that the proposed approach has good performance on segmenting the boundaries of the LV.

Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm

Dilated cardiomyopathy (DCM) is a cardiomyopathy with left ventricle or double ventricle enlargement and systolic dysfunction. It is an important cause of sudden cardiac death and heart failure and is the leading indication for cardiac transplantation. Major heart diseases like heart muscle damage and valvular problems are diagnosed using cardiac MRI. However, it takes time for cardiologists to measure DCM-related parameters to decide whether patients have this disease. We have presented a method for automatic ventricular segmentation, parameter extraction, and diagnosing DCM. In this paper, left ventricle and right ventricle are segmented by parasternal short-axis cardiac MR image sequence; then, related parameters are extracted in the end-diastole and end-systole of the heart. Machine learning classifiers use extracted parameters as input to predict normal people and patients with DCM, among which Random forest classifier gives the highest accuracy. The results show that the proposed system can be effectively utilized to detect and diagnose DCM automatically. The experimental results suggest the capabilities and advantages of the proposed method to diagnose DCM. A small amount of sample input can generate results comparable to more complex methods.

Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data

Automatic quantification of the left ventricle (LV) from cardiac magnetic resonance (CMR) images plays an important role in making the diagnosis procedure efficient, reliable, and alleviating the laborious reading work for physicians. Considerable efforts have been devoted to LV quantification using different strategies that include segmentation-based (SG) methods and the recent direct regression (DR) methods. Although both SG and DR methods have obtained great success for the task, a systematic platform to benchmark them remains absent because of differences in label information during model learning. In this paper, we conducted an unbiased evaluation and comparison of cardiac LV quantification methods that were submitted to the Left Ventricle Quantification (LVQuan) challenge, which was held in conjunction with the Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop at the MICCAI 2018. The challenge was targeted at the quantification of 1) areas of LV cavity and myocardium, 2) dimensions of the LV cavity, 3) regional wall thicknesses (RWT), and 4) the cardiac phase, from mid-ventricle short-axis CMR images. First, we constructed a public quantification dataset Cardiac-DIG with ground truth labels for both the myocardium mask and these quantification targets across the entire cardiac cycle. Then, the key techniques employed by each submission were described. Next, quantitative validation of these submissions were conducted with the constructed dataset. The evaluation results revealed that both SG and DR methods can offer good LV quantification performance, even though DR methods do not require densely labeled masks for supervision. Among the 12 submissions, the DR method LDAMT offered the best performance, with a mean estimation error of 301 mm 2 for the two areas, 2.15 mm for the cavity dimensions, 2.03 mm for RWTs, and a 9.5% error rate for the cardiac phase classification. Three of the SG methods also delivered comparable performances. Finally, we discussed the advantages and disadvantages of SG and DR methods, as well as the unsolved problems in automatic cardiac quantification for clinical practice applications.

Spatio-Temporal Multi-Task Learning for Cardiac MRI Left Ventricle Quantification

Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current clinical practice, LV quantification depends on the measurement of myocardial shape indices, which is usually achieved by manual contouring of the endo- and epicardial. However, this process subjected to inter and intra-observer variability, and it is a time-consuming and tedious task. In this article, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardiac LV morphology, regional-wall thickness (RWT), and additionally detecting the cardiac phase cycle (systole and diastole) for a given 3D Cine-magnetic resonance (MR) image sequence. We first segment cardiac LVs using an encoder-decoder network and then introduce a multitask framework to regress 11 LV indices and classify the cardiac phase, as parallel tasks during model optimization. The proposed deep learning model is based on the 3D spatio-temporal convolutions, which extract spatial and temporal features from MR images. We demonstrate the efficacy of the proposed method using cine-MR sequences of 145 subjects and comparing the performance with other state-of-the-art quantification methods. The proposed method obtained high prediction accuracy, with an average mean absolute error (MAE) of 129 mm ², 1.23 mm, 1.76 mm, Pearson correlation coefficient (PCC) of 96.4%, 87.2%, and 97.5% for LV and myocardium (Myo) cavity regions, 6 RWTs, 3 LV dimensions, and an error rate of 9.0% for phase classification. The experimental results highlight the robustness of the proposed method, despite varying degrees of cardiac morphology, image appearance, and low contrast in the cardiac MR sequences.

Multitask Learning for Estimating Multitype Cardiac Indices in MRI and CT Based on Adversarial Reverse Mapping

The estimation of multitype cardiac indices from cardiac magnetic resonance imaging (MRI) and computed tomography (CT) images attracts great attention because of its clinical potential for comprehensive function assessment. However, the most exiting model can only work in one imaging modality (MRI or CT) without transferable capability. In this article, we propose the multitask learning method with the reverse inferring for estimating multitype cardiac indices in MRI and CT. Different from the existing forward inferring methods, our method builds a reverse mapping network that maps the multitype cardiac indices to cardiac images. The task dependencies are then learned and shared to multitask learning networks using an adversarial training approach. Finally, we transfer the parameters learned from MRI to CT. A series of experiments were conducted in which we first optimized the performance of our framework via ten-fold cross-validation of over 2900 cardiac MRI images. Then, the fine-tuned network was run on an independent data set with 2360 cardiac CT images. The results of all the experiments conducted on the proposed adversarial reverse mapping show excellent performance in estimating multitype cardiac indices.

Left ventricle segmentation using a Bayesian approach with distance dependent shape priors

We propose a method for segmentation of the left ventricle in magnetic resonance cardiac images. The framework consists of an initial Bayesian segmentation of the central slice of the volume. This segmentation is used to locate a shape prior for the LV myocardial tissue. This shape prior is determined using the fact that the myocardium is approximately annular as seen in the short-axis. Then a second Bayesian segmentation is performed to obtain the final result. This procedure is repeated for the rest of the slices. An extrapolation of the area of the LV is used to determine a stopping criterion. The method was evaluated on the databases of the Cardiac Atlas project. Our results demonstrate a suitable accuracy for myocardial segmentation (≈0.8 Dice's coefficient). For the endocardium and the epicardium the Dice's coefficients are 0.94 and 0.9 respectively. The accuracy was also evaluated in terms of the Hausdorff distance and the average distance. For the myocardium we obtain 8 mm and 2 mm respectively. Our results demonstrate the capability and merits of the proposed method to estimate the structure of the LV. The method requires minimal user input and generates results with quality comparable to more complex approaches. This paper suggests a new efficient approach for automatic LV quantification based on a Bayesian technique with shape priors with errors comparable to state-of-the-art techniques.

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.

DeepCQ: Deep multi-task conditional quantification network for estimation of left ventricle parameters

BACKGROUND AND OBJECTIVE

Automatic cardiac left ventricle (LV) quantification plays an important role in assessing cardiac function. Although many advanced methods have been put forward to quantify related LV parameters, automatic cardiac LV quantification is still a challenge task due to the anatomy construction complexity of heart.

METHODS

In this work, we propose a novel deep multi-task conditional quantification learning model (DeepCQ) which contains Segmentation module, Quantification encoder, and Dynamic analysis module. Besides, we also use task uncertainty loss function to update the parameters of the network in training.

RESULTS

The proposed framework is validated on the dataset from Left Ventricle Full Quantification Challenge MICCAI 2018 (https://lvquan18.github.io/). The experimental results show that DeepCQ outperforms the other advanced methods.

CONCLUSIONS

It illustrates that our method has a great potential in comprehensive cardiac function assessment and could play an auxiliary role in clinicians' diagnosis.

Ship Segmentation in SAR Images by Improved Nonlocal Active Contour Model

Synthetic aperture radar (SAR) has been widely used in ocean surveillance. As an important part of shipping management and military applications, ship monitoring is a study hotspot in SAR image interpretation; hence, many researches focus on ship targets. Among these studies, ship segmentation is a basic work, but still remains challenging due to the speckle noise and the complicated backscattering phenomenology in SAR images. To solve the problems, this paper proposes a new method for ship segmentation by nonlocal processing. Firstly, the proposed nonlocal energy describes the nonlocal comparison of patches and optimizes regions with spatially-varying features. Secondly, we rewrite the energy functional by introducing a ratio distance defined with respect to the probability density functions of regions to overcome the influence of the multiplicative noise. Finally, the integral histogram is introduced into the pairwise interactions to fasten the speed of convergence. Several rounds of comparative experiments are implemented on real SAR data with different resolutions and bands. The results demonstrate that the proposed method is robust to the speckle noise and intensity variations and could achieve refined segmentation for ship targets.

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.

Direct Segmentation-Based Full Quantification for Left Ventricle via Deep Multi-Task Regression Learning Network

Quantitative analysis of the heart is extremely necessary and significant for detecting and diagnosing heart disease, yet there are still some challenges. In this study, we propose a new end-to-end segmentation-based deep multi-task regression learning model (Indices-JSQ) to make a holonomic quantitative analysis of the left ventricle (LV), which contains a segmentation network (Img2Contour) and multi-task regression network (Contour2Indices). First, Img2Contour, which contains a deep convolutional encoder-decoder module, is designed to obtain the LV contour. Then, the predicted contour is fed as input to Contour2Indices for full quantification. On the whole, we take into account the relationship between different tasks, which can serve as a complementary advantage. Meanwhile, instead of using images directly from the original dataset, we creatively use the segmented contour of the original image to estimate the cardiac indices to achieve better and more accurate results. We make experiments on MR sequences of 145 subjects and gain the experimental results of 157 mm ², 2.43 mm, 1.29 mm, and 0.87 on areas, dimensions, regional wall thicknesses, and Dice Metric, respectively. It intuitively shows that the proposed method outperforms the other state-of-the-art methods and demonstrates that our method has a great potential in cardiac MR images segmentation, comprehensive clinical assessment, and diagnosis.

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.

FoCA: A new framework of coupled geometric active contours for segmentation of 3D cardiac magnetic resonance images

In this paper, a new framework of coupled active contours (FoCA) is proposed for segmentation of the left ventricle myocardium, in cardiac magnetic resonance (CMR) images, without primary learning and user-driven segmentation. Primarily, we suggest a pair of coupled geometric active contours (GACs) for segmentation of the endo- and epicardial boundaries of the left ventricle in every CMR slice. The energy functional of each active contour includes the edge and shape terms of the STACS energy functional, regulator term of the local binary fitting (LBF), and new region and coupling terms. Two new patch-based region terms, inspired by LBF and piecewise model, are proposed to effectively handle intensity inhomogeneity of CMR images. Furthermore, a coupling energy term is added to the epicardial energy functional to avoid intersection with the endocardial curve. For 3D implementation, every 2D active contour in each slice is effectively jointed to the corresponding curves in the previous and next slices (of the same volume) by using a new coupling energy term, obtained by extending the 2D length-shortening regulator. Also, the initial contour and algorithm parameters are automatically regulated. Finally, 3D+t implementation is performed by using the sequential initialization method. Experimental results demonstrated that the proposed method provided superior solution quality compared to a large number of counterpart algorithms by using two well-known frequently-used databases.

Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning

Cardiac indices estimation is of great importance during identification and diagnosis of cardiac disease in clinical routine. However, estimation of multitype cardiac indices with consistently reliable and high accuracy is still a great challenge due to the high variability of cardiac structures and the complexity of temporal dynamics in cardiac MR sequences. While efforts have been devoted into cardiac volumes estimation through feature engineering followed by a independent regression model, these methods suffer from the vulnerable feature representation and incompatible regression model. In this paper, we propose a semi-automated method for multitype cardiac indices estimation. After the manual labeling of two landmarks for ROI cropping, an integrated deep neural network Indices-Net is designed to jointly learn the representation and regression models. It comprises two tightly-coupled networks, such as a deep convolution autoencoder for cardiac image representation, and a multiple output convolution neural network for indices regression. Joint learning of the two networks effectively enhances the expressiveness of image representation with respect to cardiac indices, and the compatibility between image representation and indices regression, thus leading to accurate and reliable estimations for all the cardiac indices. When applied with five-fold cross validation on MR images of 145 subjects, Indices-Net achieves consistently low estimation error for LV wall thicknesses (1.44 ± 0.71 mm) and areas of cavity and myocardium (204 ± 133 mm²). It outperforms, with significant error reductions, segmentation method (55.1% and 17.4%), and two-phase direct volume-only methods (12.7% and 14.6%) for wall thicknesses and areas, respectively. These advantages endow the proposed method a great potential in clinical cardiac function assessment.

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.

Myocardial Infarct Segmentation From Magnetic Resonance Images for Personalized Modeling of Cardiac Electrophysiology

Accurate representation of myocardial infarct geometry is crucial to patient-specific computational modeling of the heart in ischemic cardiomyopathy. We have developed a methodology for segmentation of left ventricular (LV) infarct from clinically acquired, two-dimensional (2D), late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) images, for personalized modeling of ventricular electrophysiology. The infarct segmentation was expressed as a continuous min-cut optimization problem, which was solved using its dual formulation, the continuous max-flow (CMF). The optimization objective comprised of a smoothness term, and a data term that quantified the similarity between image intensity histograms of segmented regions and those of a set of training images. A manual segmentation of the LV myocardium was used to initialize and constrain the developed method. The three-dimensional geometry of infarct was reconstructed from its segmentation using an implicit, shape-based interpolation method. The proposed methodology was extensively evaluated using metrics based on geometry, and outcomes of individualized electrophysiological simulations of cardiac dys(function). Several existing LV infarct segmentation approaches were implemented, and compared with the proposed method. Our results demonstrated that the CMF method was more accurate than the existing approaches in reproducing expert manual LV infarct segmentations, and in electrophysiological simulations. The infarct segmentation method we have developed and comprehensively evaluated in this study constitutes an important step in advancing clinical applications of personalized simulations of cardiac electrophysiology.

Segmentation of myocardium from cardiac MR images using a novel dynamic programming based segmentation method

PURPOSE

Myocardium segmentation in cardiac magnetic resonance (MR) images plays a vital role in clinical diagnosis of the cardiovascular diseases. Because of the low contrast and large variation in intensity and shapes, myocardium segmentation has been a challenging task. A dynamic programming (DP) based segmentation method, incorporating the likelihood and shape information of the myocardium, is developed for segmenting myocardium in cardiac MR images.

METHODS

The endocardium, i.e., the left ventricle blood cavity, is segmented for initialization, and then the optimal epicardium contour is determined using the polar-transformed image and DP scheme. In the DP segmentation scheme, three techniques are proposed to improve the segmentation performance: (1) the likelihood image of the myocardium is constructed to define the external cost in the DP, thus the cost function incorporates prior probability estimation; (2) the adaptive search range is introduced to determine the polar-transformed image, thereby excluding irrelevant tissues; (3) the connectivity constrained DP algorithm is developed to obtain an optimal closed contour. Four metrics, including the Dice metric (Dice), root mean squared error (RMSE), reliability, and correlation coefficient, are used to assess the segmentation accuracy. The authors evaluated the performance of the proposed method on a private dataset and the MICCAI 2009 challenge dataset. The authors also explored the effects of the three new techniques of the DP scheme in the proposed method.

RESULTS

For the qualitative evaluation, the segmentation results of the proposed method were clinically acceptable. For the quantitative evaluation, the mean (Dice) for the endocardium and epicardium was 0.892 and 0.927, respectively; the mean RMSE was 2.30 mm for the endocardium and 2.39 mm for the epicardium. In addition, the three new techniques in the proposed DP scheme, i.e., the likelihood image of the myocardium, the adaptive search range, and the connectivity constrained DP algorithm, improved the segmentation performance for the epicardium with 0.029, 0.047, and 0.007 in terms of the Dice and 0.98, 1.31, and 0.21 mm in terms of the RMSE, respectively.

CONCLUSIONS

The three techniques (the likelihood image of the myocardium, the adaptive search range, and the connectivity constrained DP algorithm) can improve the segmentation ability of the DP method, and the proposed method with these techniques has the ability to achieve the acceptable segmentation result of the myocardium in cardiac MR images. Therefore, the proposed method would be useful in clinical diagnosis of the cardiovascular diseases.

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.

Real-time aortic valve segmentation from transesophageal echocardiography sequence

PURPOSE

Geometric features of the aortic valve play an important role in many applications, such as the clinical diagnostics, shape modeling and image-guided cardiac interventions, especially for the transcatheter aortic valve implantation (TAVI) procedure. However, few works have been reported on the topic of aortic valve segmentation from transesophageal echocardiography (TEE) sequences. To obtain accurate segmentation results and further provide valid support for TAVI, this paper presents a real-time method for segmenting the aortic valve from intraoperative, short-axis view TEE sequences, using an improved probability estimation and continuous max-flow (CMF) approach.

METHODS

The proposed segmentation method includes two key stages: (1) In the probability estimation stage, five different prior frames spanning a cardiac circle are firstly selected with the aortic valve manually segmented by an expert. Then, the improved composite probability estimation (CPE) and single probability estimation (SPE) over the five prior frames are, respectively, constructed based on their radial average intensity and radial distance. (2) In the energy function construction stage, the similarity metric is calculated to find out the matching exponents between the current input TEE frame and the prior frames. The typical foreground and background intensities of prior images are therefore used to construct the corresponding energy function. Finally, the CMF approach, accelerated with a graphic processing unit (GPU), is employed to achieve the aortic valve contours in real time.

RESULTS

The evaluation study contained 30 sequences, with each containing 62-146 short-axis TEE frames. The results were compared with the manual segmentation (ground truth). The average symmetric contour distance (ASCD), dice metric (DM) and the reliability of the algorithm reached 0.85 ± 0.21 mm, 0.96 ± 0.01 and 0.90 (d = 0.95), respectively, and the computation time was 57.04 ± 8.98 ms per frame.

CONCLUSION

The experiment results reveal that the proposed method can achieve accurate and real-time segmentation of aortic valve from TEE sequence of short-axis view.

A novel aortic valve segmentation from ultrasound image using continuous max-flow approach

Geometric features of aortic valve can be applied in diagnostic, modeling and image-guided cardiac intervention, however methods to accurately and effectively delineate aortic valve from ultrasound (US) image are not well addressed. This paper proposes a novel aortic valve segmentation algorithm for intra-operative 2D short-axis US image using probability estimation and continuous max-flow (CMF) approach. The algorithm first calculates composite probability estimation (CPE) and single probability estimation (SPE) over 5 prior images based on both intensity and distance to the corresponding centroid, then the energy function for the current input image is constructed, followed by a Graphic Processing Unit (GPU) accelerated CMF approach to achieve aortic valve contours in approximately real time. Quantitative evaluations over 270 images acquired from 3 subjects indicated the results of the algorithm had good correlation with the manual segmentation results (ground truth) by an expert. The Average Symmetric Contour Distance (ASCD), Dice Metric (DM), and Reliability were employed to evaluate our algorithm, and the evaluation results of these three metrics were 1.79±0.46 (in pixels), 0.96±0.01 and 0.84 (d=0.95) respectively, where the computational time was 39.23±5.02 ms per frame.