A novel model-based 3D +time left ventricular segmentation technique

Multiresolution Mutual Assistance Network for Cardiac Magnetic Resonance Images Segmentation

The automatic segmentation of cardiac magnetic resonance (MR) images is the basis for the diagnosis of cardiac-related diseases. However, the segmentation of cardiac MR images is a challenging task due to the inhomogeneity of MR images intensity distribution and the unclear boundaries between adjacent tissues. In this paper, we propose a novel multiresolution mutual assistance network (MMA-Net) for cardiac MR images segmentation. It is mainly composed of multibranch input module, multiresolution mutual assistance module, and multilabel deep supervision. First, the multibranch input module helps the network to extract local and global features more pertinently. Then, the multiresolution mutual assistance module implements multiresolution feature interaction and progressively improves semantic features to more completely express the information of the tissue. Finally, the multilabel deep supervision is proposed to generate the final segmentation map. We compare with state-of-the-art medical image segmentation methods on the medical image computing and computer-assisted intervention (MICCAI) automated cardiac diagnosis challenge datasets and the MICCAI atrial segmentation challenge datasets. The mean dice scores of our method in the left atrium, right ventricle, myocardium, and left ventricle are 0.919, 0.920, 0.881, and 0.960, respectively. The analysis of evaluation indicators and segmentation results shows that our method achieves the best performance in cardiac magnetic resonance images segmentation.

Ultrafast Computation of Left Ventricular Ejection Fraction by Using Temporal Intensity Variation in Cine Cardiac Magnetic Resonance

Cardiac magnetic resonance enables comprehensive cardiac evaluation; however, intense time and labor requirements for data acquisition and processing have discouraged many clinicians from using it. We have developed an alternative image-processing algorithm that requires minimal user interaction: an ultrafast algorithm that computes left ventricular ejection fraction (LVEF) by using temporal intensity variation in cine balanced steady-state free precession (bSSFP) short-axis images, with or without contrast medium. We evaluated the algorithm's performance against an expert observer's analysis for segmenting the LV cavity in 65 study participants (LVEF range, 12%-70%). In 12 instances, contrast medium was administered before cine imaging. Bland-Altman analysis revealed quantitative effects of LV basal, midcavity, and apical morphologic variation on the algorithm's accuracy. Total computation time for the LV stack was <2.5 seconds. The algorithm accurately delineated endocardial boundaries in 1,132 of 1,216 slices (93%). When contours in the extreme basal and apical slices were not adequate, they were replaced with manually drawn contours. The Bland-Altman mean differences were <1.2 mL (0.8%) for end-diastolic volume, <5 mL (6%) for end-systolic volume, and <3% for LVEF. Standard deviation of the difference was ≤4.1% of LV volume for all sections except the midcavity in end-systole (8.3% of end-systolic volume). We conclude that temporal intensity variation-based ultrafast LVEF computation is clinically accurate across a range of LV shapes and wall motions and is suitable for postcontrast cine SSFP imaging. Our algorithm enables real-time processing of cine bSSFP images on a commercial scanner console within 3 seconds in an unobtrusive automated process.

Deep Learning for Quantitative Cardiac MRI

OBJECTIVE. The recent advancement of deep learning techniques has profoundly impacted research on quantitative cardiac MRI analysis. The purpose of this article is to introduce the concept of deep learning, review its current applications on quantitative cardiac MRI, and discuss its limitations and challenges. CONCLUSION. Deep learning has shown state-of-the-art performance on quantitative analysis of multiple cardiac MRI sequences and holds great promise for future use in clinical practice and scientific research.

Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical need

Cardiac MRI (CMR) allows non-invasive, non-ionizing assessment of cardiac function and anatomy in patients with congenital heart disease (CHD). The utility of CMR as a non-invasive imaging tool for evaluation of CHD have been growing exponentially over the past decade. The algorithms based on artificial intelligence (AI), and in particular, deep learning, have rapidly become a methodology of choice for analyzing CMR. A wide range of applications for AI have been developed to tackle challenges in various aspects of CMR, and significant advances have also been made from image acquisition to image analysis and diagnosis. We include an overview of AI definitions, different architectures, and details on well-known methods. This paper reviews the major deep learning concepts used for analyses of patients with CHD. In the end, we have summarized a list of open challenges and concerns to be considered for future studies.

RIANet: Recurrent interleaved attention network for cardiac MRI segmentation

BACKGROUND

Segmentation of anatomical structures of the heart from cardiac magnetic resonance images (MRI) has a significant impact on the quantitative analysis of the cardiac contractile function. Although deep convolutional neural networks (ConvNets) have achieved considerable success in medical imaging segmentation, it is still a challenging task for existing deep ConvNets to precisely and automatically segment multiple heart structures from cardiac MRI. This paper presents a novel recurrent interleaved attention network (RIANet) to comprehensively tackle this issue.

METHOD

The proposed RIANet can efficiently reuse parameters to encode richer representative features via introducing a recurrent feedback structure, Clique Block, which incorporates both forward and backward connections between different layers with the same resolution. Further, we integrate a plug-and-play interleaved attention (IA) block to modulate the information passed to the decoding stage of RIANet by effectively fusing multi-level contextual information. In addition, we improve the discrimination capability of our RIANet through a deep supervision mechanism with weighted losses.

RESULTS

The performance of RIANet has been extensively validated in the segmentation contest of the ACDC 2017 challenge held in conjunction with MICCAI 2017, with mean Dice scores of 0.942 (left ventricular), 0.923 (right ventricular) and 0.910 (myocardium) for cardiac MRI segmentation. Besides, we visualize intermediate features of our RIANet using guided backpropagation, which can intuitively depict the effects of our proposed components in feature representation.

CONCLUSION

Experimental results demonstrate that our RIANet have achieved competitive segmentation results with fewer parameters compared with the state-of-the-art approaches, corroborating the effectiveness and robustness of our proposed RIANet.

Automatic localization of the left ventricle in cardiac MRI images using deep learning

Automatic localization of the left ventricle (LV) in cardiac MRI images is an essential step for automatic segmentation, functional analysis, and content based retrieval of cardiac images. In this paper, we introduce a new approach based on deep Convolutional Neural Network (CNN) to localize the LV in cardiac MRI in short axis views. A six-layer CNN with different kernel sizes was employed for feature extraction, followed by Softmax fully connected layer for classification. The pyramids of scales analysis was introduced in order to take account of the different sizes of the heart. A publically-available database of 33 patients was used for learning and testing. The proposed method was able it localize the LV with 98.66%, 83.91% and 99.07% for accuracy, sensitivity and specificity respectively.

Fast segmentation of the left ventricle in cardiac MRI using dynamic programming

BACKGROUND AND OBJECTIVE

The segmentation of the left ventricle (LV) in cardiac magnetic resonance imaging is a necessary step for the analysis and diagnosis of cardiac function. In most clinical setups, this step is still manually performed by cardiologists, which is time-consuming and laborious. This paper proposes a fast system for the segmentation of the LV that significantly reduces human intervention.

METHODS

A dynamic programming approach is used to obtain the border of the LV. Using very simple assumptions about the expected shape and location of the segmentation, this system is able to deal with many of the challenges associated with this problem. The system was evaluated on two public datasets: one with 33 patients, comprising a total of 660 magnetic resonance volumes and another with 45 patients, comprising a total of 90 volumes. Quantitative evaluation of the segmentation accuracy and computational complexity was performed.

RESULTS

The proposed system is able to segment a whole volume in 1.5 seconds and achieves an average Dice similarity coefficient of 86.0% and an average perpendicular distance of 2.4 mm, which compares favorably with other state-of-the-art methods.

CONCLUSIONS

A system for the segmentation of the left ventricle in cardiac magnetic resonance imaging is proposed. It is a fast framework that significantly reduces the amount of time and work required of cardiologists.

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.

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.

Clinical feasibility of a myocardial signal intensity threshold-based semi-automated cardiac magnetic resonance segmentation method

OBJECTIVES

To assess the accuracy and efficiency of a threshold-based, semi-automated cardiac MRI segmentation algorithm in comparison with conventional contour-based segmentation and aortic flow measurements.

METHODS

Short-axis cine images of 148 patients (55 ± 18 years, 81 men) were used to evaluate left ventricular (LV) volumes and mass (LVM) using conventional and threshold-based segmentations. Phase-contrast images were used to independently measure stroke volume (SV). LV parameters were evaluated by two independent readers.

RESULTS

Evaluation times using the conventional and threshold-based methods were 8.4 ± 1.9 and 4.2 ± 1.3 min, respectively (P < 0.0001). LV parameters measured by the conventional and threshold-based methods, respectively, were end-diastolic volume (EDV) 146 ± 59 and 134 ± 53 ml; end-systolic volume (ESV) 64 ± 47 and 59 ± 46 ml; SV 82 ± 29 and 74 ± 28 ml (flow-based 74 ± 30 ml); ejection fraction (EF) 59 ± 16 and 58 ± 17%; and LVM 141 ± 55 and 159 ± 58 g. Significant differences between the conventional and threshold-based methods were observed in EDV, ESV, and LVM mesurements; SV from threshold-based and flow-based measurements were in agreement (P > 0.05) but were significantly different from conventional analysis (P < 0.05). Excellent inter-observer agreement was observed.

CONCLUSIONS

Threshold-based LV segmentation provides improved accuracy and faster assessment compared to conventional contour-based methods.

KEY POINTS

• Threshold-based left ventricular segmentation provides time-efficient assessment of left ventricular parameters • The threshold-based method can discriminate between blood and papillary muscles • This method provides improved accuracy compared to aortic flow measurements as a reference.

Automatic segmentation of the left ventricle in cardiac MRI using local binary fitting model and dynamic programming techniques

Segmentation of the left ventricle is very important to quantitatively analyze global and regional cardiac function from magnetic resonance. The aim of this study is to develop a novel algorithm for segmenting left ventricle on short-axis cardiac magnetic resonance images (MRI) to improve the performance of computer-aided diagnosis (CAD) systems. In this research, an automatic segmentation method for left ventricle is proposed on the basis of local binary fitting (LBF) model and dynamic programming techniques. The validation experiments are performed on a pool of data sets of 45 cases. For both endo- and epi-cardial contours of our results, percentage of good contours is about 93.5%, the average perpendicular distance are about 2 mm. The overlapping dice metric is about 0.91. The regression and determination coefficient between the experts and our proposed method on the LV mass is 1.038 and 0.9033, respectively; they are 1.076 and 0.9386 for ejection fraction (EF). The proposed segmentation method shows the better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.

Simplified post processing of cine DENSE cardiovascular magnetic resonance for quantification of cardiac mechanics

BACKGROUND

Cardiovascular magnetic resonance using displacement encoding with stimulated echoes (DENSE) is capable of assessing advanced measures of cardiac mechanics such as strain and torsion. A potential hurdle to widespread clinical adoption of DENSE is the time required to manually segment the myocardium during post-processing of the images. To overcome this hurdle, we proposed a radical approach in which only three contours per image slice are required for post-processing (instead of the typical 30-40 contours per image slice). We hypothesized that peak left ventricular circumferential, longitudinal and radial strains and torsion could be accurately quantified using this simplified analysis.

METHODS AND RESULTS

We tested our hypothesis on a large multi-institutional dataset consisting of 541 DENSE image slices from 135 mice and 234 DENSE image slices from 62 humans. We compared measures of cardiac mechanics derived from the simplified post-processing to those derived from original post-processing utilizing the full set of 30-40 manually-defined contours per image slice. Accuracy was assessed with Bland-Altman limits of agreement and summarized with a modified coefficient of variation. The simplified technique showed high accuracy with all coefficients of variation less than 10% in humans and 6% in mice. The accuracy of the simplified technique was also superior to two previously published semi-automated analysis techniques for DENSE post-processing.

CONCLUSIONS

Accurate measures of cardiac mechanics can be derived from DENSE cardiac magnetic resonance in both humans and mice using a simplified technique to reduce post-processing time by approximately 94%. These findings demonstrate that quantifying cardiac mechanics from DENSE data is simple enough to be integrated into the clinical workflow.

Automatic functional analysis of left ventricle in cardiac cine MRI

RATIONALE AND OBJECTIVES

A fully automated left ventricle segmentation method for the functional analysis of cine short axis (SAX) magnetic resonance (MR) images was developed, and its performance evaluated with 133 studies of subjects with diverse pathology: ischemic heart failure (n=34), non-ischemic heart failure (n=30), hypertrophy (n=32), and healthy (n=37).

MATERIALS AND METHODS

The proposed automatic method locates the left ventricle (LV), then for each image detects the contours of the endocardium, epicardium, papillary muscles and trabeculations. Manually and automatically determined contours and functional parameters were compared quantitatively.

RESULTS

There was no significant difference between automatically and manually determined end systolic volume (ESV), end diastolic volume (EDV), ejection fraction (EF) and left ventricular mass (LVM) for each of the four groups (paired sample t-test, α=0.05). The automatically determined functional parameters showed high correlations with those derived from manual contours, and the Bland-Altman analysis biases were small (1.51 mL, 1.69 mL, -0.02%, -0.66 g for ESV, EDV, EF and LVM, respectively).

CONCLUSIONS

The proposed technique automatically and rapidly detects endocardial, epicardial, papillary muscles' and trabeculations' contours providing accurate and reproducible quantitative MRI parameters, including LV mass and EF.

Performance evaluation of point matching algorithms for left ventricle motion analysis in MRI

Finding correspondences between contour points in consecutive frames is crucial for the left ventricular motion analysis. In many medical applications, point correspondences can be determined by using distinctive anatomical features, called anatomical landmarks. However, in the case of cardiac images, these landmarks are scarce and insufficient for the registration. Several methods have been proposed using semi-landmarks, but this may lead to incorrect correspondences. This paper proposes and evaluates the performance of three point matching algorithm. Results show that the matching by resampling method leads to the best overall correspondences and compares favorably with the performance of a state of the art shape alignment algorithm [9].

4D statistical shape modeling of the left ventricle in cardiac MR images

PURPOSE

Statistical shape models have shown improved reliability and consistency in cardiac image segmentation. They incorporate a sufficient amount of a priori knowledge from the training datasets and solve some major problems such as noise and image artifacts or partial volume effect. In this paper, we construct a 4D statistical model of the left ventricle using human cardiac short-axis MR images.

METHODS

Kernel PCA is utilized to explore the nonlinear variation of a population. The distribution of the landmarks is divided into the inter- and intra-subject subspaces. We compare the result of Kernel PCA with linear PCA and ICA for each of these subspaces. The initial atlas in natural coordinate system is built for the end-diastolic frame. The landmarks extracted from it are propagated to all frames of all datasets. We apply the 4D KPCA-based ASM for segmentation of all phases of a cardiac cycle and compare it with the conventional ASM.

RESULTS

The proposed statistical model is evaluated by calculating the compactness capacity, specificity and generalization ability measures. We investigate the behavior of the nonlinear model for different values of the kernel parameter. The results show that the model built by KPCA is less compact than PCA but more compact than ICA. Although for a constant number of modes the reconstruction error is a little higher for the KPCA-based statistical model, it produces a statistical model with substantially better specificity than PCA- and ICA-based models.

CONCLUSION

Quantitative analysis of the results demonstrates that our method improves the segmentation accuracy.