Short-axis Magnetic Resonance Images Research Articles

A Multi-Stage Automatic Method Based on a Combination of Fully Convolutional Networks for Cardiac Segmentation in Short-Axis MRI

Magnetic resonance imaging (MRI) is a non-invasive technique used in cardiac diagnosis. Using it, specialists can measure the masses and volumes of the right ventricle (RV), left ventricular cavity (LVC), and myocardium (MYO). Segmenting these structures is an important step before this measurement. However, this process can be laborious and error-prone when done manually. This paper proposes a multi-stage method for cardiac segmentation in short-axis MRI based on fully convolutional networks (FCNs). This automatic method comprises three main stages: (1) the extraction of a region of interest (ROI); (2) MYO and LVC segmentation using a proposed FCN called EAIS-Net; and (3) the RV segmentation using another proposed FCN called IRAX-Net. The proposed method was tested with the ACDC and M&Ms datasets. The main evaluation metrics are end-diastolic (ED) and end-systolic (ES) Dice. For the ACDC dataset, the Dice results (ED and ES, respectively) are 0.960 and 0.904 for the LVC, 0.880 and 0.892 for the MYO, and 0.910 and 0.860 for the RV. For the M&Ms dataset, the ED and ES Dices are 0.861 and 0.805 for the LVC, 0.733 and 0.759 for the MYO, and 0.721 and 0.694 for the RV. These results confirm the feasibility of the proposed method.

Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

The automatic localization of the left ventricle (LV) in short-axis magnetic resonance (MR) images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest (ROI). The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration. Nevertheless, this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices. Thus, this study proposed a region-based convolutional network (Faster R-CNN) for the LV localization from short-axis cardiac MRI images using a region proposal network (RPN) integrated with deep feature classification and regression. The model was trained using images with corresponding bounding boxes (labels) around the LV, and various experiments were applied to select the appropriate layers and set the suitable hyper-parameters. The experimental findings show that the proposed model was adequate, with accuracy, precision, recall, and F1 score values of 0.91, 0.94, 0.95, and 0.95, respectively. This model also allows the cropping of the detected area of LV, which is vital in reducing the computational cost and time during segmentation and classification procedures. Therefore, it would be an ideal model and clinically applicable for diagnosing cardiac diseases.

Cardiac MRI segmentation with sparse annotations: Ensembling deep learning uncertainty and shape priors.

The performance of deep learning for cardiac magnetic resonance imaging (MRI) segmentation is oftentimes degraded when using small datasets and sparse annotations for training or adapting a pre-trained model to previously unseen datasets. Here, we developed and evaluated an approach to addressing some of these issues to facilitate broader use of deep learning for short-axis cardiac MRI segmentation. We developed a globally optimal label fusion (GOLF) algorithm that enforced spatial smoothness to generate consensus segmentation from segmentation predictions provided by a deep learning ensemble algorithm. The GOLF consensus was entered into an uncertainty-guided coupled continuous kernel cut (ugCCKC) algorithm that employed normalized cut, image-grid continuous regularization, and "nesting" and circular shape priors of the left ventricular myocardium (LVM) and cavity (LVC). In addition, the uncertainty measurements derived from the segmentation predictions were used to constrain the similarity of GOLF and final segmentation. We optimized ugCCKC through upper bound relaxation, for which we developed an efficient coupled continuous max-flow algorithm implemented in an iterative manner. We showed that GOLF yielded average symmetric surface distance (ASSD) 0.2-0.8mm lower than an averaging method with higher or similar Dice similarity coefficient (DSC). We also demonstrated that ugCCKC incorporating the shape priors improved DSC by 0.01-0.05 and reduced ASSD by 0.1-0.9mm. In addition, we integrated GOLF and ugCCKC into a deep learning ensemble algorithm by refining the segmentation of an unannotated dataset and using the refined segmentation to update the trained models. With the proposed framework, we demonstrated the capability of using relatively small datasets (5-10 subjects) with sparse (5-25% slices labeled) annotations to train a deep learning algorithm, while achieving DSC of 0.871-0.893 for LVM and 0.933-0.959 for LVC on the LVQuan dataset, and these were 0.844-0.871 for LVM and 0.923-0.931 for LVC on the ACDC dataset. Furthermore, we showed that the proposed approach can be adapted to substantially alleviate the domain shift issue. Moreover, we calculated a number of commonly used LV function measurements using the derived segmentation and observed strong correlations (Pearson r=0.77-1.00, p<0.001) between algorithm and manual LV function analyses. These results suggest that the developed approaches can be used to facilitate broader application of deep learning in research and clinical cardiac MR imaging workflow.

Combining UNet 3+ and Transformer for Left Ventricle Segmentation via Signed Distance and Focal Loss

Left ventricle (LV) segmentation of cardiac magnetic resonance (MR) images is essential for evaluating cardiac function parameters and diagnosing cardiovascular diseases (CVDs). Accurate LV segmentation remains a challenge because of the large differences in cardiac structures in different research subjects. In this work, a network based on an encoder–decoder architecture for automatic LV segmentation of short-axis cardiac MR images is proposed. It combines UNet 3+ and Transformer to jointly predict the segmentation masks and signed distance maps (SDM). UNet 3+ can extract coarse-grained semantics and fine-grained details from full scales, while a Transformer is used to extract global features from cardiac MR images. It solves the problem of low segmentation accuracy caused by blurred LV edge information. Meanwhile, the SDM provides a shape-aware representation for segmentation. The performance of the proposed network is validated on the 2018 MICCAI Left Ventricle Segmentation Challenge dataset. The five-fold cross-validation evaluation was performed on 145 clinical subjects, and the average dice metric, Jaccard coefficient, accuracy, and positive predictive value reached 0.908, 0.834, 0.979, and 0.903, respectively, showing a better performance than that of other mainstream ones.

Automatic Left Ventricle Segmentation from Short-Axis Cardiac MRI Images Based on Fully Convolutional Neural Network.

Background: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing cardiovascular diseases. LV clinical metrics such as LV volume, LV mass and ejection fraction (EF) are frequently extracted based on the LV segmentation from short-axis MRI images. Manual segmentation to assess such functions is tedious and time-consuming for medical experts to diagnose cardiac pathologies. Therefore, a fully automated LV segmentation technique is required to assist medical experts in working more efficiently. Method: This paper proposes a fully convolutional network (FCN) architecture for automatic LV segmentation from short-axis MRI images. Several experiments were conducted in the training phase to compare the performance of the network and the U-Net model with various hyper-parameters, including optimization algorithms, epochs, learning rate, and mini-batch size. In addition, a class weighting method was introduced to avoid having a high imbalance of pixels in the classes of image’s labels since the number of background pixels was significantly higher than the number of LV and myocardium pixels. Furthermore, effective image conversion with pixel normalization was applied to obtain exact features representing target organs (LV and myocardium). The segmentation models were trained and tested on a public dataset, namely the evaluation of myocardial infarction from the delayed-enhancement cardiac MRI (EMIDEC) dataset. Results: The dice metric, Jaccard index, sensitivity, and specificity were used to evaluate the network’s performance, with values of 0.93, 0.87, 0.98, and 0.94, respectively. Based on the experimental results, the proposed network outperforms the standard U-Net model and is an advanced fully automated method in terms of segmentation performance. Conclusion: This proposed method is applicable in clinical practice for doctors to diagnose cardiac diseases from short-axis MRI images.

Left ventricular non-compaction cardiomyopathy automatic diagnosis using a deep learning approach

Background and Objective: Left ventricular non-compaction (LVNC) is an uncommon cardiomyopathy characterised by a thick and spongy left ventricle wall caused by the high presence of trabeculae (hyper-trabeculation). Recently, the percentage of the trabecular volume to the total volume of the external wall of the left ventricle (VT%) has been proposed to diagnose this illness.Methods: This paper presents the use of a deep learning-based method to measure the (VT%) value and diagnose this rare cardiomyopathy.The population used in this research was composed of 277 patients suffering from hypertrophic cardiomyopathy. 134 patients only suffered hypertrophic cardiomyopathy, and 143 also suffered left ventricular non-compaction.Our deep learning solution is based on a 2D U-Net. This artificial neural network (ANN) was trained on short-axis magnetic resonance imaging to segment the left ventricle’s internal cavity, external wall, and trabecular tissue. 5-fold cross-validation was performed to ensure the robustness of the results. The Dice coefficient of the three classes was computed as a measure of the precision of the segmentation.Based on this segmentation, the percentage of the trabecular volume (VT%) was computed. Two specialist cardiologists rated the segmentation produced by the neural network for 25 patients to evaluate the clinical validity of the outputs. The computed VT% was used to automatically diagnose the 277 patients depending on whether or not a given threshold was exceeded. A receiver operating characteristic analysis was also performed.Results: According to the cross-validation results, the average and standard deviation of the Dice coefficient for the internal cavity, external wall, and trabeculae were 0.96±0.00, 0.89±0.00, and 0.84±0.00, respectively. The cardiologists rated 99.5% of the evaluated segmentations as clinically valid for diagnosis, outperforming existing automatic traditional tools. The area under the ROC curve was 0.94 (95% confidence interval, 0.91–0.96). The accuracy, sensitivity, and specificity values of diagnosis using a threshold of 25% were 0.87, 0.93, and 0.80, respectively.Conclusions: The U-Net neural network can achieve excellent results in the delineation of different cardiac structures of short-axis cardiac MRI. The high-quality segmentation allows for the correct measurement of left ventricular hyper-trabeculation and a definitive diagnosis of LVNC illness. Using this kind of solution could lead to more objective and faster analysis, reducing human error and time spent by cardiologists.

Unchanged right ventricular strain in repaired tetralogy of Fallot after pulmonary valve replacement with radial long-axis cine magnetic resonance images

We measured right ventricular (RV) strain by applying a novel postprocessing technique to conventional short-axis cine magnetic resonance imaging in the repaired tetralogy of Fallot (TOF) and investigated whether pulmonary valve replacement (PVR) changes the RV strain. Twenty-four patients with repaired TOF who underwent PVR and 16 healthy controls were enrolled. Global maximum and minimum principal strains (GPSmax, GPSmin) and global circumferential and longitudinal strains (GCS, GLS) were measured from short-axis cine images reconstructed radially along the long axis. Strain parameters before and after PVR were compared using paired t-tests. One-way ANOVA with Tukey post-hoc analysis was used for comparisons between the before and after PVR groups and the control group. There were no differences in strain parameters before and after PVR. The GPSmax before PVR was lower than that in the control group (P = 0.002). Before and after PVR, GCSs were higher and GLSs were lower than those in the control group (before and after GCSs: P = 0.002 for both, before and after GLSs: P < 0.0001 and P = 0.0003). RV strains from radially reconstructed short-axis cine images revealed unchanged myocardial motion after PVR. When compared to the control group, changes in GCS and GLS in TOF patients before and after PVR might be due to RV remodeling.

A Deep Learning Framework for Image Super-Resolution for Late Gadolinium Enhanced Cardiac MRI.

Cardiac magnetic resonance imaging (MRI) provides 3D images with high-resolution in-plane information, however, they are known to have low through-plane resolution due to the trade-off between resolution, image acquisition time and signal-to-noise ratio. This results in anisotropic 3D images which could lead to difficulty in diagnosis, especially in late gadolinium enhanced (LGE) cardiac MRI, which is the reference imaging modality for locating the extent of myocardial fibrosis in various cardiovascular diseases like myocardial infarction and atrial fibrillation. To address this issue, we propose a self-supervised deep learning-based approach to enhance the through-plane resolution of the LGE MRI images. We train a convolutional neural network (CNN) model on randomly extracted patches of short-axis LGE MRI images and this trained CNN model is used to leverage the information learnt from the high-resolution in-plane data to improve the through-plane resolution. We conducted experiments on LGE MRI dataset made available through the 2018 atrial segmentation challenge. Our proposed method achieved a mean peak signal-to-noise-ratio (PSNR) of 36.99 and 35.92 and a mean structural similarity index measure (SSIM) of 0.9 and 0.84 on training the CNN model using low-resolution images downsampled by a scale factor of 2 and 4, respectively.

DBAN: Adversarial Network With Multi-Scale Features for Cardiac MRI Segmentation.

With the development of medical artificial intelligence, automatic magnetic resonance image (MRI) segmentation method is quite desirable. Inspired by the power of deep neural networks, a novel deep adversarial network, dilated block adversarial network (DBAN), is proposed to perform left ventricle, right ventricle, and myocardium segmentation in short-axis cardiac MRI. DBAN contains a segmentor along with a discriminator. In the segmentor, the dilated block (DB) is proposed to capture, and aggregate multi-scale features. The segmentor can produce segmentation probability maps while the discriminator can differentiate the segmentation probability map, and the ground truth at the pixel level. In addition, confidence probability maps generated by the discriminator can guide the segmentor to modify segmentation probability maps. Extensive experiments demonstrate that DBAN has achieved the state-of-the-art performance on the ACDC dataset. Quantitative analyses indicate that cardiac function indices from DBAN are similar to those from clinical experts. Therefore, DBAN can be a potential candidate for short-axis cardiac MRI segmentation in clinical applications.

Segmentation of the cardiac ventricle using two layer level sets with prior shape constraint

The cardiac Left Ventricle (LV) segmentation is still challengeable due to the complex anatomical structure surrounding the LV and intensity overlaps caused by intensity inhomogeneities, which widely exists in cardiac Magnetic Resonance (MR) images. To overcome these problems, a shape prior constrained two-layer level set method is proposed to simultaneously extract the endocardium and epicardium of the LV from cardiac short-axis MR images. The proposed method is validated on MR images from the LV challenge of MICCAI 2009. Quantitative and qualitative experiments and clinical metrics demonstrate that the proposed method has better performance than some representative methods in term of Dice Similarity Coefficient (DSC), Average Perpendicular Distance (APD), Ejection Fraction (EF), and LV mass. Experimental results demonstrate that the proposed method has the priority of preserving cardiac anatomical structure and improving segmentation accuracy.

Multiscale attention guided U-Net architecture for cardiac segmentation in short-axis MRI images

Background and Objective: Automatic cardiac segmentation plays an utmost role in the diagnosis and quantification of cardiovascular diseases. Methods: This paper proposes a new cardiac segmentation method in short-axis Magnetic Resonance Imaging (MRI) images, called attention U-Net architecture with input image pyramid and deep supervised output layers (AID), which can fully-automatically learn to pay attention to target structures of various sizes and shapes. During each training process, the model continues to learn how to emphasize the desired features and suppress irrelevant areas in the original images, effectively improving the accuracy of cardiac segmentation. At the same time, we introduce the Focal Tversky Loss (FTL), which can effectively solve the problem of high imbalance in the amount of data between the target class and the background class during cardiac image segmentation. In order to obtain a better representation of intermediate features, we add a multi-scale input pyramid to the attention network. Results: The proposed cardiac segmentation technique is tested on the public Left Ventricle Segmentation Challenge (LVSC) dataset, which is shown to achieve 0.75, 0.87 and 0.92 for Jaccard Index, Sensitivity and Specificity, respectively. Experimental results demonstrate that the proposed method is able to improve the segmentation accuracy compared with the standard U-Net, and achieves comparable performance to the most advanced fully-automated methods. Conclusions: Given its effectiveness and advantages, the proposed method can facilitate cardiac segmentation in short-axis MRI images in clinical practice.

Automatic left ventricle segmentation in short-axis MRI using deep convolutional neural networks and central-line guided level set approach

In the clinical diagnosis of cardiovascular diseases, left ventricle (LV) segmentation in cardiac magnetic resonance images (MRI) is an indispensable procedure for doctors. To reduce the time needed for diagnosis, we develop an automatic LV segmentation method by integrating the convolutional neural network (CNN) with the level set approach. Firstly, a CNN based myocardial central-line detection algorithm was proposed to replace the manual initialization process for traditional level set approaches. Secondly, we present a novel central-line guided level set approach (CGLS) for delineating the myocardium region. In particular, we incorporate the myocardial central-line into the level set energy formulation as a constraint term. It plays two important roles in the iterative process: restricting the zero-level contour to stay around the myocardial central-line and preserving the anatomical geometry of myocardium segmentation result. In experiments, our method yields results as below: (1) 1.74 mm and 2.06 mm in terms of epicardium and endocardium perpendicular distance on MICCAI 2009 dataset, (2) 0.955 and 0.853 in terms of LV and myocardium Dice metric at the end-diastole on ACDC MICCAI 2017 dataset. The experimental data demonstrate that our method outperforms some state-of-the-art methods and achieves a good agreement with the manual segmentation results.

Multi-Scale Feature Fusion Convolutional Neural Network for Concurrent Segmentation of Left Ventricle and Myocardium in Cardiac MR Images

Accurate segmentation of the blood pool of left ventricle (LV) and myocardium (or left ventricular epicardium, MYO) from cardiac magnetic resonance (MR) can help doctors to quantify LV ejection fraction and myocardial deformation. To reduce doctor’s burden of manual segmentation, in this study, we propose an automated and concurrent segmentation method of the LV and MYO. First, we employ a convolutional neural network (CNN) architecture to extract the region of interest (ROI) from short-axis cardiac cine MR images as a preprocessing step. Next, we present a multi-scale feature fusion (MSFF) CNN with a new weighted Dice index (WDI) loss function to get the concurrent segmentation of the LV and MYO. We use MSFF modules with three scales to extract different features, and then concatenate feature maps by the short and long skip connections in the encoder and decoder path to capture more complete context information and geometry structure for better segmentation. Finally, we compare the proposed method with Fully Convolutional Networks (FCN) and U-Net on the combined cardiac datasets from MICCAI 2009 and ACDC 2017. Experimental results demonstrate that the proposed method could perform effectively on LV and MYOs segmentation in the combined datasets, indicating its potential for clinical application.

Direct estimation of left ventricular ejection fraction via a cardiac cycle feature learning architecture

The left ventricular ejection fraction is of significant importance for the early identification and diagnosis of cardiac disease. However, estimation of the left ventricular ejection fraction with consistently reliable and high accuracy remains a great challenge, owing to the high variability of cardiac structures and the complexity of the temporal dynamics in the cardiac magnetic resonance imaging sequences. The popular methods of left ventricular ejection fraction estimation rely on the left ventricular volume. Thus, strong prior knowledge is often necessary, impeding the ease of use of the existing methods as clinical tools. In this study, we propose a cardiac cycle feature learning architecture for achieving an accurate and reliable estimation of the left ventricular ejection fraction. The proposed method constructs a cardiac cycle extraction module that generates and analyzes an optical flow to obtain the cardiac cycle of all images, a motion feature fusion and extraction module for temporal modeling of the cardiac sequences, and a fully connected regression module for achieving a direct estimation. Experiments on 2900 left ventricle segments of 145 subjects from short-axis magnetic resonance imaging sequences of multiple lengths prove that our proposed method achieves reliable performance (correlation coefficient: 0.946; mean absolute error 2.67; standard deviation: 3.23). As compared with the current state-of-the-art method, our proposed method improves the performance by approximately 3% insofar as the mean absolute error. As the first solution for estimating the left ventricular ejection fraction directly, our proposed method demonstrates great potential for future clinical applications.

Automatic segmentation of left ventricle from cardiac MRI via deep learning and region constrained dynamic programming

Segmentation of the left ventricle from cardiac magnetic resonance images (MRI) is an essential step to quantitatively analyze global and regional cardiac function. The aim of this study is to develop a novel and robust algorithm which can improve the accuracy of automatic left ventricle segmentation on short-axis cardiac MRI. The database used in this study are 900 cardiac MRI cases from Hubei Cancer Hospital. Three key techniques are developed in this segmentation algorithm: (1) deep learning schema is designed for coarse segmentation of LV (left ventricle) myocardial images, (2) trimmed mean approach is employed to build a proper region for epicardial contour searching, and (3) an edge map with non-maxima gradient suppression approach is put forward to improve the dynamic programming to derive the epicardial boundary. The accuracy of cardiac boundaries and cardiac functional parameters are benchmarked against those derived from expert manual boundary delineations and derived measurements on 900 samples of Hubei Cancer Hospital. The average Jaccard (overlap) indexes for endo and epicardial contours are 0.80 and 0.76. Bland-Altman analysis for LVEDV (LV End-diastolic Volume), LVESV (LV End-systolic Volume), LVSV (LV 232 Stroke volume), LVEF (LV Ejection Fraction), LVM-DP (LV mass in diastolic phase) and LVM-SP (LV mass in systolic phase), yielded biases of 4.15 ml, 0.26 ml, 3.89 ml, 0.32%, −7.24 g and -0.08 g, respectively, and limits of agreement of ±30.32 ml, ±11.10 ml, ±24.31 ml, ±12.89%, ±24.66 g and ±21.93 g, respectively. Our hybrid scheme combining deep learning and dynamic programming for automatic segmentation of the LV from cardiac MRI datasets is effective and robust and can compute LV functional indexes from population imaging.

3-D Consistent and Robust Segmentation of Cardiac Images by Deep Learning With Spatial Propagation.

We propose a method based on deep learning to perform cardiac segmentation on short axis Magnetic resonance imaging stacks iteratively from the top slice (around the base) to the bottom slice (around the apex). At each iteration, a novel variant of the U-net is applied to propagate the segmentation of a slice to the adjacent slice below it. In other words, the prediction of a segmentation of a slice is dependent upon the already existing segmentation of an adjacent slice. The 3-D consistency is hence explicitly enforced. The method is trained on a large database of 3078 cases from the U.K. Biobank. It is then tested on the 756 different cases from the U.K. Biobank and three other state-of-the-art cohorts (ACDC with 100 cases, Sunnybrook with 30 cases, and RVSC with 16 cases). Results comparable or even better than the state of the art in terms of distance measures are achieved. They also emphasize the assets of our method, namely, enhanced spatial consistency (currently neither considered nor achieved by the state of the art), and the generalization ability to unseen cases even from other databases.

Inhomogeneity correction and hybrid-based segmentation in cardiac MRI

Image segmentation is an important step in medical image analysis and segmentation of ventricles in Cardiac Magnetic Resonance (CMR) images is challenging due to an in-built artefact called intensity-inhomogeneity. The short-axis cine Magnetic Resonance Images (MRI) recorded under a steady-state free precision protocol were corrected for intensity-inhomogeneity using Bias Corrected Fuzzy C-Means (BCFCM) method, Level Set (LS) and Multiplicative Intrinsic Component Optimisation (MICO) methods. The statistical measures show that bias correction by BCFCM has better performance than MICO and LS. In addition, the original and bias corrected images are validated by Multifractal Analysis (MFA). The results show that in bias corrected images, the low frequency components are removed thereby enhancing the sharpness of the ventricular boundaries. Further, ventricular segmentation is performed using the proposed automatic hybrid Sobel edge detector with optimised level set method. The validation parameters of segmented results show that the ventricular detection in bias corrected images matches better with ground truth.

Group-wise similarity registration of point sets using Student's t-mixture model for statistical shape models.

A probabilistic group-wise similarity registration technique based on Student's t-mixture model (TMM) and a multi-resolution extension of the same (mr-TMM) are proposed in this study, to robustly align shapes and establish valid correspondences, for the purpose of training statistical shape models (SSMs). Shape analysis across large cohorts requires automatic generation of the requisite training sets. Automated segmentation and landmarking of medical images often result in shapes with varying proportions of outliers and consequently require a robust method of alignment and correspondence estimation. Both TMM and mrTMM are validated by comparison with state-of-the-art registration algorithms based on Gaussian mixture models (GMMs), using both synthetic and clinical data. Four clinical data sets are used for validation: (a) 2D femoral heads (K= 1000 samples generated from DXA images of healthy subjects); (b) control-hippocampi (K= 50 samples generated from T1-weighted magnetic resonance (MR) images of healthy subjects); (c) MCI-hippocampi (K= 28 samples generated from MR images of patients diagnosed with mild cognitive impairment); and (d) heart shapes comprising left and right ventricular endocardium and epicardium (K= 30 samples generated from short-axis MR images of: 10 healthy subjects, 10 patients diagnosed with pulmonary hypertension and 10 diagnosed with hypertrophic cardiomyopathy). The proposed methods significantly outperformed the state-of-the-art in terms of registration accuracy in the experiments involving synthetic data, with mrTMM offering significant improvement over TMM. With the clinical data, both methods performed comparably to the state-of-the-art for the hippocampi and heart data sets, which contained few outliers. They outperformed the state-of-the-art for the femur data set, containing large proportions of outliers, in terms of alignment accuracy, and the quality of SSMs trained, quantified in terms of generalization, compactness and specificity.

Left ventricle segmentation via two-layer level sets with circular shape constraint

This paper proposes a circular shape constraint and a novel two-layer level set method for the segmentation of the left ventricle (LV) from short-axis magnetic resonance images without training any shape models. Since the shape of LV throughout the apex-base axis is close to a ring shape, we propose a circle fitting term in the level set framework to detect the endocardium. The circle fitting term imposes a penalty on the evolving contour from its fitting circle, and thereby handles quite well with issues in LV segmentation, especially the presence of outflow track in basal slices and the intensity overlap between TPM and the myocardium. To extract the whole myocardium, the circle fitting term is incorporated into two-layer level set method. The endocardium and epicardium are respectively represented by two specified level contours of the level set function, which are evolved by an edge-based and a region-based active contour model. The proposed method has been quantitatively validated on the public data set from MICCAI 2009 challenge on the LV segmentation. Experimental results and comparisons with state-of-the-art demonstrate the accuracy and robustness of our method.

Segmentation of the right ventricle in MRI images using a dual active shape model

Active shape models (ASM) showed to have potential for segmenting the right ventricle (RV) in cardiac magnetic resonance images (MRIs). Nevertheless, the large variability and complexity of the RV shape do not allow for concisely capturing all possible shape variations among patients and anatomical cross-sections. Noticeably, the latter increases the number of iterations required to converge to a proper solution and reduces the segmentation accuracy. In this study, the authors propose a new ASM framework that can model the RV shape in short-axis cardiac MRI images. In this framework, the RV contour is split into two simpler segments, septal (SP) and free wall, whose shape variations are independently modelled using two separate (dual) ASM models. The contour splitting is done at the location of the RV insertion points into the SP wall. Further, instead of using the conventional Procrustes method, the RV contours are aligned using the Bookstein coordinate transformation, which uses the RV insertion points as landmarks to linearly align the RV contours. The results from a dataset of 14 patients show that the proposed framework outperforms the conventional ASM framework and can model complex RV shape variation with more accuracy and in less iteration steps.