Segmentation of Left Ventricle in Cardiac Cine MRI: An Automatic Image-Driven Method. In: Ayache N, Delingette H, Sermesant M, editors. Functional Imaging and Modeling of the Heart. Berlin: Springer Berlin Heidelberg; 2009. pp. 339-47. [DOI: 10.1007/978-3-642-01932-6_37]

Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning

Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. We chose four standard views in pediatric cardiac ultrasound to identify atrial septal defects; the four standard views were as follows: subcostal sagittal view of the atrium septum (subSAS), apical four-chamber view (A4C), the low parasternal four-chamber view (LPS4C), and parasternal short-axis view of large artery (PSAX). We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). In our model, we present a block random selection, maximal agreement decision, and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. We validate our model using our private dataset by five cross-validation. For ASD detection, we achieve 89.33 ± 3.13 AUC, 84.95 ± 3.88 accuracy, 85.70 ± 4.91 sensitivity, 81.51 ± 8.15 specificity, and 81.99 ± 5.30 F1 score. The proposed model is a multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors.

Left Ventricle Segmentation in Cardiac MR Images via an Improved ResUnet

Cardiovascular diseases are reported as the leading cause of death around the world. Automatic segmentation of the left ventricle (LV) from magnetic resonance (MR) images is essential for an early diagnosis. An enhanced ResUnet is proposed in this paper to improve the performance of extracting LV endocardium and epicardium from MR images, improving the accuracy of the model by introducing a medium skip connection for the contracting path and a short skip connection for the residual unit. Also, a depth-wise separable convolution replaces the typical convolution operation to improve training efficiency. In the MICCAI 2009 LV segmentation challenge test dataset, the percentages of "good" contours, dice metric, and average perpendicular distance of endocardium (epicardium) are 99.12% ± 2.29%(100% ± 0%), 0.93 ± 0.02 (0.96 ± 0.01), and 1.60 ± 0.42 mm (1.37 ± 0.23 mm), respectively. Experimental results demonstrate that the proposed model obtains promising performance and outperforms state-of-the-art methods. By incorporating these various skip connections, the segmentation accuracy of the model is significantly improved, while the depth-wise separable convolution also improves the model efficiency.

Technical Note: Fully automatic segmental relaxometry (FASTR) for cardiac magnetic resonance T1 mapping

PURPOSE

Cardiac relaxometry techniques, particularly T1 mapping, have recently gained clinical importance in various cardiac pathologies. Myocardial T1 and extracellular volume are usually calculated from manual identification of left ventricular epicardial and endocardial regions. This is a laborious process, particularly for large volume studies. Here we present a fully automated relaxometry framework (FASTR) for segmental analysis of T1 maps (both native and postcontrast) and partition coefficient (λ).

METHODS

Patients (N = 11) were imaged postacute myocardial infarction on a 1.5T clinical scanner. The scan protocol involved CINE-SSFP imaging, native, and post-contrast T1 mapping using the Modified Look-Locker Inversion (MOLLI) recovery sequence. FASTR consisted of automatic myocardial segmentation of spatio-temporally coregistered CINE images as an initial guess, followed by refinement of the contours on the T1 maps to derive segmental T1 and λ. T1 and λ were then compared to those obtained from two trained expert observers.

RESULTS

Robust endocardial and epicardial contours were achieved on T1 maps despite the presence of infarcted tissue. Relative to experts, FASTR resulted in myocardial Dice coefficients (native T1: 0.752 ± 0.041; postcontrast T1: 0.751 ± 0.057) that were comparable to interobserver Dice (native T1: 0.803 ± 0.045; postcontrast T1: 0.799 ± 0.054). There were strong correlations observed for T1 and λ derived from experts and FASTR (native T1: r = 0.83; postcontrast T1: r = 0.87; λ: r = 0.78; P < 0.0001), which were comparable to inter-expert correlation coefficients (native T1: r = 0.90; postcontrast T1: r = 0.93; λ: r = 0.80; P < 0.0001).

CONCLUSIONS

Our fully automated framework, FASTR, can generate accurate myocardial segmentations for native and postcontrast MOLLI T1 analysis without the need for manual intervention. Such a design is appealing for high volume clinical protocols.

Deep Convolutional Neural Networks for left ventricle segmentation

Left ventricle (LV) segmentation is crucial for quantitative cardiac function analysis. Manual segmentation of the endocardium and epicardium is highly cumbersome; physicians limit delineation to the end-diastolic and end-systolic phases. A fully automated system could provide an analysis of cardiac morphology for all phases in a much shorter time. Most of the current LV segmentation methods are semi-automated and require error prone manual initialization. A fully-automated LV segmentation method would expedite the functional analysis of the LV, reduce subjectivity and improve patient experience. We automatically segment the LV wall in cardiac MRI images with a Deep Convolutional Neural Network (DCNN). This algorithm first calculates the probability of a pixel belonging to the LV wall or background and then generates a label based on those probabilities without manual initialization. We then compare these results to the results obtained with another DCNN initialization method using Gabor filters. With Gabor DCNN we obtain an accuracy of 0.97, specificity of 0.984, sensitivity of 0.841 and mean accuracy of 0.902. This shows that Gabor filters perform better than random filters in the DCNN for LV segmentation.

Cardiovascular magnetic resonance guided ablation and intra-procedural visualization of evolving radiofrequency lesions in the left ventricle

BACKGROUND

Radiofrequency (RF) ablation has become a mainstay of treatment for ventricular tachycardia, yet adequate lesion formation remains challenging. This study aims to comprehensively describe the composition and evolution of acute left ventricular (LV) lesions using native-contrast cardiovascular magnetic resonance (CMR) during CMR-guided ablation procedures.

METHODS

RF ablation was performed using an actively-tracked CMR-enabled catheter guided into the LV of 12 healthy swine to create 14 RF ablation lesions. T2 maps were acquired immediately post-ablation to visualize myocardial edema at the ablation sites and T1-weighted inversion recovery prepared balanced steady-state free precession (IR-SSFP) imaging was used to visualize the lesions. These sequences were repeated concurrently to assess the physiological response following ablation for up to approximately 3 h. Multi-contrast late enhancement (MCLE) imaging was performed to confirm the final pattern of ablation, which was then validated using gross pathology and histology.

RESULTS

Edema at the ablation site was detected in T2 maps acquired as early as 3 min post-ablation. Acute T2-derived edematous regions consistently encompassed the T1-derived lesions, and expanded significantly throughout the 3-h period post-ablation to 1.7 ± 0.2 times their baseline volumes (mean ± SE, estimated using a linear mixed model determined from n = 13 lesions). T1-derived lesions remained approximately stable in volume throughout the same time frame, decreasing to 0.9 ± 0.1 times the baseline volume (mean ± SE, estimated using a linear mixed model, n = 9 lesions).

CONCLUSIONS

Combining native T1- and T2-based imaging showed that distinctive regions of ablation injury are reflected by these contrast mechanisms, and these regions evolve separately throughout the time period of an intervention. An integrated description of the T1-derived lesion and T2-derived edema provides a detailed picture of acute lesion composition that would be most clinically useful during an ablation case.

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 segmentation of left ventricle cavity from short-axis cardiac magnetic resonance images

In this paper, a computational framework is proposed to perform a fully automatic segmentation of the left ventricle (LV) cavity from short-axis cardiac magnetic resonance (CMR) images. In the initial phase, the region of interest (ROI) is automatically identified on the first image frame of the CMR slices. This is done by partitioning the image into different regions using a standard fuzzy c-means (FCM) clustering algorithm where the LV region is identified according to its intensity, size and circularity in the image. Next, LV segmentation is performed within the identified ROI by using a novel clustering method that utilizes an objective functional with a dissimilarity measure that incorporates a circular shape function. This circular shape-constrained FCM algorithm is able to differentiate pixels with similar intensity but are located in different regions (e.g. LV cavity and non-LV cavity), thus improving the accuracy of the segmentation even in the presence of papillary muscles. In the final step, the segmented LV cavity is propagated to the adjacent image frame to act as the ROI. The segmentation and ROI propagation are then iteratively executed until the segmentation has been performed for the whole cardiac sequence. Experiment results using the LV Segmentation Challenge validation datasets show that our proposed framework can achieve an average perpendicular distance (APD) shift of 2.23 ± 0.50 mm and the Dice metric (DM) index of 0.89 ± 0.03, which is comparable to the existing cutting edge methods. The added advantage over state of the art is that our approach is fully automatic, does not need manual initialization and does not require a prior trained model.

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.

Feasibility Study of Respiratory Motion Modeling Based Correction for MRI-Guided Intracardiac Interventional Procedures

GOAL

The purpose of this study is to improve the accuracy of interventional catheter guidance during intracardiac procedures. Specifically, the use of preprocedural magnetic resonance roadmap images for interventional guidance has limited anatomical accuracy due to intraprocedural respiratory motion of the heart. Therefore, we propose to build a novel respiratory motion model to compensate for this motion-induced error during magnetic resonance imaging (MRI)-guided procedures.

METHODS

We acquire 2-D real-time free-breathing images to characterize the respiratory motion, and build a smooth motion model via registration of 3-D prior roadmap images to the real-time images within a novel principal axes frame of reference. The model is subsequently used to correct the interventional catheter positions with respect to the anatomy of the heart.

RESULTS

We demonstrate that the proposed modeling framework can lead to smoother motion models, and potentially lead to more accurate motion estimates. Specifically, MRI-guided intracardiac ablations were performed in six preclinical animal experiments. Then, from retrospective analysis, the proposed motion modeling technique showed the potential to achieve a 27% improvement in ablation targeting accuracy.

CONCLUSION

The feasibility of a respiratory motion model-based correction framework has been successfully demonstrated.

SIGNIFICANCE

The improvement in ablation accuracy may lead to significant improvements in success rate and patient outcomes for MRI-guided intracardiac procedures.

An SPCNN-GVF-based approach for the automatic segmentation of left ventricle in cardiac cine MR images

PURPOSE

Accurate segmentation of left ventricle (LV) is essential for the cardiac function analysis. However, it is labor intensive and time consuming for radiologists to delineate LV boundary manually. In this paper, we present a novel self-correcting framework for the fully automatic LV segmentation.

METHODS

Firstly, a time-domain method is designed to extract a rectangular region of interest around the heart. Then, the simplified pulse-coupled neural network (SPCNN) is employed to locate the LV cavity. Different from the existing approaches, SPCNN can realize the self-correcting segmentation due to its parameter controllability. Subsequently, the post-processing based on the maximum gradient searching is proposed to obtain the accurate endocardium. Finally, a new external force based on the shape similarity is defined and integrated into the gradient vector flow (GVF) snake with the balloon force to segment the epicardium.

RESULTS

We obtain encouraging segmentation results tested on the database provided by MICCAI 2009. The average percentage of good contours is 92.26 %, the average perpendicular distance is 2.38 mm, and the overlapping dice metric is 0.89. Besides, the experiment results show good correlations between the automatic segmentation and the manual delineation (for the LV ejection fraction and the LV myocardial mass, the correlation coefficients R are 0.9683 and 0.9278, respectively).

CONCLUSION

We propose an effective and fast method combing the SPCNN and the improved GVF for the automatic segmentation of LV.

Segmentation of the left ventricular endocardium from magnetic resonance images by using different statistical shape models

We evaluate in this paper different strategies for the construction of a statistical shape model (SSM) of the left ventricle (LV) to be used for segmentation in cardiac magnetic resonance (CMR) images. From a large database of LV surfaces obtained throughout the cardiac cycle from 3D echocardiographic (3DE) LV images, different LV shape models were built by varying the considered phase in the cardiac cycle and the registration procedure employed for surface alignment. Principal component analysis was computed to describe the statistical variability of the SSMs, which were then deformed by applying an active shape model (ASM) approach to segment the LV endocardium in CMR images of 45 patients. Segmentation performance was evaluated by comparing LV volumes derived by ASM segmentation with different SSMs and those obtained by manual tracing, considered as a reference. A high correlation (r(2)>0.92) was found in all cases, with better results when using the SSM models comprising more than one frame of the cardiac cycle.

An Algorithm for the Segmentation of Highly Abnormal Hearts Using a Generic Statistical Shape Model

Statistical shape models (SSMs) have been widely employed in cardiac image segmentation. However, in conditions that induce severe shape abnormality and remodeling, such as in the case of pulmonary hypertension (PH) or hypertrophic cardiomyopathy (HCM), a single SSM is rarely capable of capturing the anatomical variability in the extremes of the distribution. This work presents a new algorithm for the segmentation of severely abnormal hearts. The algorithm is highly flexible, as it does not require a priori knowledge of the involved pathology or any specific parameter tuning to be applied to the cardiac image under analysis. The fundamental idea is to approximate the gross effect of the abnormality with a virtual remodeling transformation between the patient-specific geometry and the average shape of the reference model (e.g., average normal morphology). To define this mapping, a set of landmark points are automatically identified during boundary point search, by estimating the reliability of the candidate points. With the obtained transformation, the feature points extracted from the patient image volume are then projected onto the space of the reference SSM, where the model is used to effectively constrain and guide the segmentation process. The extracted shape in the reference space is finally propagated back to the original image of the abnormal heart to obtain the final segmentation. Detailed validation with patients diagnosed with PH and HCM shows the robustness and flexibility of the technique for the segmentation of highly abnormal hearts of different pathologies.

Segmentation of the left ventricle using distance regularized two-layer level set approach

We propose a novel two-layer level set approach for segmentation of the left ventricle (LV) from cardiac magnetic resonance (CMR) short-axis images. In our method, endocardium and epicardium are represented by two specified level contours of a level set function. Segmentation of the LV is formulated as a problem of optimizing the level set function such that these two level contours best fit the epicardium and endocardium. More importantly, a distance regularization (DR) constraint on the level contours is introduced to preserve smoothly varying distance between them. This DR constraint leads to a desirable interaction between the level contours that contributes to maintain the anatomical geometry of the endocardium and epicardium. The negative influence of intensity inhomogeneities on image segmentation are overcome by using a data term derived from a local intensity clustering property. Our method is quantitatively validated by experiments on the datasets for the MICCAI grand challenge on left ventricular segmentation, which demonstrates the advantages of our method in terms of segmentation accuracy and consistency with anatomical geometry.

Automatic motion analysis system for pyloric flow in ultrasonic videos

Ultrasonography has been widely used to evaluate duodenogastric reflux (DGR). But to the best of our knowledge, no automatic analysis system was developed to realize the quantitative computer-aided analysis. In this paper, we propose a system to perform the automatic detection of DGR in the ultrasonic image sequences by applying the automatic motion analysis. The motion field is estimated based on image velocimetry. Then, an intelligent motion analysis is applied. For the DGR detection, the motion and structural information is combined to analyze the transploric motion of the fluid. In order to test the performance of the proposed system, we designed the experiment with the real and synthetic ultrasonic data. The proposed system achieved a good performance in the DGR detection. The automatic results were accordant with the gold standard in analyzing the fluid motion. The proposed system is supposed to be a promising tool for the study and evaluation of DGR.

Automatic left ventricle segmentation in cardiac MRI using topological stable-state thresholding and region restricted dynamic programming

RATIONALE AND OBJECTIVES

Segmentation of the left ventricle (LV) is very important in the assessment of cardiac functional parameters. The aim of this study is to develop a novel and robust algorithm which can improve the accuracy of automatic LV segmentation on short-axis cardiac magnetic resonance images (MRI).

MATERIALS AND METHODS

The database used in this study consists of 45 cases obtained from the Sunnybrook Health Sciences Centre. The 45 cases contain 12 ischemic heart failures, 12 non-ischemic heart failures, 12 LV hypertrophies, and 9 normal cases. Three key techniques are developed in this segmentation algorithm: 1) topological stable-state thresholding method is proposed to refine the endocardial contour, 2) an edge map with non-maxima gradient suppression approach, and 3) a region-restricted technique that is proposed to improve the dynamic programming to derive the epicardial boundary.

RESULTS

The validation experiments were performed on a pool of data sets of 45 cases. For both endo- and epicardial contours of our results, percentage of good contours is about 91%, the average perpendicular distance is about 2 mm, and the overlapping dice metric is about 0.91. The regression and determination coefficient for the experts and our proposed method on the ejection fraction is 1.05 and 0.9048, respectively; they are 0.98 and 0.8221 for LV mass.

CONCLUSIONS

An automatic method using topological stable-state thresholding and region restricted dynamic programming has been proposed to segment left ventricle in short-axis cardiac MRI. Evaluation results indicate that the proposed segmentation method can improve the accuracy and robust of left ventricle segmentation. The proposed segmentation approach shows the better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.

An image-based comprehensive approach for automatic segmentation of left ventricle from cardiac short axis cine MR images

Segmentation of the left ventricle is important in the assessment of cardiac functional parameters. Manual segmentation of cardiac cine MR images for acquiring these parameters is time-consuming. Accuracy and automation are the two important criteria in improving cardiac image segmentation methods. In this paper, we present a comprehensive approach to segment the left ventricle from short axis cine cardiac MR images automatically. Our method incorporates a number of image processing and analysis techniques including thresholding, edge detection, mathematical morphology, and image filtering to build an efficient process flow. This process flow makes use of various features in cardiac MR images to achieve high accurate segmentation results. Our method was tested on 45 clinical short axis cine cardiac images and the results are compared with manual delineated ground truth (average perpendicular distance of contours near 2 mm and mean myocardium mass overlapping over 90%). This approach provides cardiac radiologists a practical method for an accurate segmentation of the left ventricle.

Pattern recognition of abnormal left ventricle wall motion in cardiac MR

There are four main problems that limit application of pattern recognition techniques for recognition of abnormal cardiac left ventricle (LV) wall motion: (1) Normalization of the LV's size, shape, intensity level and position; (2) defining a spatial correspondence between phases and subjects; (3) extracting features; (4) and discriminating abnormal from normal wall motion. Solving these four problems is required for application of pattern recognition techniques to classify the normal and abnormal LV wall motion. In this work, we introduce a normalization scheme to solve the first and second problems. With this scheme, LVs are normalized to the same position, size, and intensity level. Using the normalized images, we proposed an intra-segment classification criterion based on a correlation measure to solve the third and fourth problems. Application of the method to recognition of abnormal cardiac MR LV wall motion showed promising results.