Three-dimensional segmentation of the left ventricle in late gadolinium enhanced MR images of chronic infarction combining long- and short-axis information

Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge

Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, compared with the other sequences LGE CMR images with gold standard labels are particularly limited. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation focusing on myocardial wall of the left ventricle and blood cavity of the two ventricles. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the ventricle segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are unsupervised domain adaptation (UDA) methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particularly, the top-ranking algorithms from both the supervised and UDA methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challenge continues as an ongoing resource, and the gold standard segmentation as well as the MS-CMR images of both the training and test data are available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mscmrseg/).

Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation

In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificial intelligence semisupervised self-training algorithm into the pathological tissue image segmentation problem. An experimental group is designed to collect sample images and the algorithm proposed in this article is used to perform image segmentation to achieve a better visual experience and images. Although there is no general image segmentation theory, many scholars have been committed to applying new concepts and new methods to image segmentation in recent years and combining specific theoretical image segmentation methods has achieved good application results in image segmentation. For example, wavelet analysis, wavelet transform, neural networks, and genetic algorithms can effectively improve the segmentation effect. The results of the Seg cutting method designed in this article show that, in retinal blood vessel segmentation results on a database of healthy people, the sensitivity value is 0.941633, the false-positive rate is 0.952933, the specificity is 0.956787, and the accuracy rate is 0.96182, which are all higher than those in other methods. Image cutting methods such as FNN, CNN, and AWN have addressed the case tissue image cutting problem. Using the Seg cutting method designed in this article to segment the retinal blood vessels on a diabetes patient database, the sensitivity value is 0.8106, the false-positive rate is 0.0511, the specificity is 0.9712, the accuracy is 0.9421, and the false-positive rate is omitted. The false-positive rate is lower than AWN, and other indicators are higher than FNN, CNN, AWN, and other image cutting methods. The application of artificial intelligence-based semisupervised self-training algorithms in pathological tissue image segmentation is realized.

Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives

Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.

Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images

The author proposes a method for simultaneous registration and segmentation of multi-source images, using the multivariate mixture model (MvMM) and maximum of log-likelihood (LL) framework. Specifically, the method is applied to the problem of myocardial segmentation combining the complementary information from multi-sequence (MS) cardiac magnetic resonance (CMR) images. For the image misalignment and incongruent data, the MvMM is formulated with transformations and is further generalized for dealing with the hetero-coverage multi-modality images (HC-MMIs). The segmentation of MvMM is performed in a virtual common space, to which all the images and misaligned slices are simultaneously registered. Furthermore, this common space can be divided into a number of sub-regions, each of which contains congruent data, thus the HC-MMIs can be modeled using a set of conventional MvMMs. Results show that MvMM obtained significantly better performance compared to the conventional approaches and demonstrated good potential for scar quantification as well as myocardial segmentation. The generalized MvMM has also demonstrated better robustness in the incongruent data, where some images may not fully cover the region of interest, and the full coverage can only be reconstructed combining the images from multiple sources.

An Overview on Image Registration Techniques for Cardiac Diagnosis and Treatment

Image registration has been used for a wide variety of tasks within cardiovascular imaging. This study aims to provide an overview of the existing image registration methods to assist researchers and impart valuable resource for studying the existing methods or developing new methods and evaluation strategies for cardiac image registration. For the cardiac diagnosis and treatment strategy, image registration and fusion can provide complementary information to the physician by using the integrated image from these two modalities. This review also contains a description of various imaging techniques to provide an appreciation of the problems associated with implementing image registration, particularly for cardiac pathology intervention and treatments.

Myocardium segmentation from DE MRI with guided random walks and sparse shape representation

PURPOSE

For patients with myocardial infarction (MI), delayed enhancement (DE) cardiovascular magnetic resonance imaging (MRI) is a sensitive and well-validated technique for the detection and visualization of MI. The myocardium viability assessment with DE MRI is important in diagnosis and treatment management, where myocardium segmentation is a prerequisite. However, few academic works have focused on automated myocardium segmentation from DE images. In this study, we aim to develop an automatic myocardium segmentation algorithm that targets DE images.

METHODS

We propose a segmentation framework based on both prior shape knowledge and image intensity. Instead of the strong request of the pre-segmentation of cine MRI in the same session, we use the sparse representation method to model the myocardium shape. Data from the Cardiac MR Left Ventricle Segmentation Challenge (2009) are used to build the shape template repository. The method of guided random walks is used to integrate the shape model and intensity information. An iterative approach is used to gradually improve the results.

RESULTS

The proposed method was tested on the DE MRI data from 30 MI patients. The proposed method achieved Dice similarity coefficients (DSC) of 74.60 ± 7.79% with 201 shape templates and 73.56 ± 6.32% with 56 shape templates, which were close to the inter-observer difference (73.94 ± 5.12%). To test the generalization of the proposed method to routine clinical images, the DE images of 10 successive new patients were collected, which were unseen during the method development and parameter tuning, and a DSC of 76.02 ± 7.43% was achieved.

CONCLUSION

The authors propose a novel approach for the segmentation of myocardium from DE MRI by using the sparse representation-based shape model and guided random walks. The sparse representation method effectively models the prior shape with a small number of shape templates, and the proposed method has the potential to achieve clinically relevant results.

Myocardium Segmentation From DE MRI Using Multicomponent Gaussian Mixture Model and Coupled Level Set

Objective: In this paper, we propose a fully automatic framework for myocardium segmentation of delayed-enhancement (DE) MRI images without relying on prior patient-specific information. Methods: We employ a multicomponent Gaussian mixture model to deal with the intensity heterogeneity of myocardium caused by the infarcts. To differentiate the myocardium from other tissues with similar intensities, while at the same time maintain spatial continuity, we introduce a coupled level set (CLS) to regularize the posterior probability. The CLS, as a spatial regularization, can be adapted to the image characteristics dynamically. We also introduce an image intensity gradient based term into the CLS, adding an extra force to the posterior probability based framework, to improve the accuracy of myocardium boundary delineation. The prebuilt atlases are propagated to the target image to initialize the framework. Results: The proposed method was tested on datasets of 22 clinical cases, and achieved Dice similarity coefficients of 87.43 ± 5.62% (endocardium), 90.53 ± 3.20% (epicardium) and 73.58 ± 5.58% (myocardium), which have outperformed three variants of the classic segmentation methods. Conclusion: The results can provide a benchmark for the myocardial segmentation in the literature. Significance: DE MRI provides an important tool to assess the viability of myocardium. The accurate segmentation of myocardium, which is a prerequisite for further quantitative analysis of myocardial infarction (MI) region, can provide important support for the diagnosis and treatment management for MI patients.Objective: In this paper, we propose a fully automatic framework for myocardium segmentation of delayed-enhancement (DE) MRI images without relying on prior patient-specific information. Methods: We employ a multicomponent Gaussian mixture model to deal with the intensity heterogeneity of myocardium caused by the infarcts. To differentiate the myocardium from other tissues with similar intensities, while at the same time maintain spatial continuity, we introduce a coupled level set (CLS) to regularize the posterior probability. The CLS, as a spatial regularization, can be adapted to the image characteristics dynamically. We also introduce an image intensity gradient based term into the CLS, adding an extra force to the posterior probability based framework, to improve the accuracy of myocardium boundary delineation. The prebuilt atlases are propagated to the target image to initialize the framework. Results: The proposed method was tested on datasets of 22 clinical cases, and achieved Dice similarity coefficients of 87.43 ± 5.62% (endocardium), 90.53 ± 3.20% (epicardium) and 73.58 ± 5.58% (myocardium), which have outperformed three variants of the classic segmentation methods. Conclusion: The results can provide a benchmark for the myocardial segmentation in the literature. Significance: DE MRI provides an important tool to assess the viability of myocardium. The accurate segmentation of myocardium, which is a prerequisite for further quantitative analysis of myocardial infarction (MI) region, can provide important support for the diagnosis and treatment management for MI patients.

Accuracy of respiratory motion measurement of 4D-MRI: A comparison between cine and sequential acquisition

PURPOSE

The authors have recently developed a cine-mode T2*/T1-weighted 4D-MRI technique and a sequential-mode T2-weighted 4D-MRI technique for imaging respiratory motion. This study aims at investigating which 4D-MRI image acquisition mode, cine or sequential, provides more accurate measurement of organ motion during respiration.

METHODS

A 4D digital extended cardiac-torso (XCAT) human phantom with a hypothesized tumor was used to simulate the image acquisition and the 4D-MRI reconstruction. The respiratory motion was controlled by the given breathing signal profiles. The tumor was manipulated to move continuously with the surrounding tissue. The motion trajectories were measured from both sequential- and cine-mode 4D-MRI images. The measured trajectories were compared with the average trajectory calculated from the input profiles, which was used as references. The error in 4D-MRI tumor motion trajectory (E) was determined. In addition, the corresponding respiratory motion amplitudes of all the selected 2D images for 4D reconstruction were recorded. Each of the amplitude was compared with the amplitude of its associated bin on the average breathing curve. The mean differences from the average breathing curve across all slice positions (D) were calculated. A total of 500 simulated respiratory profiles with a wide range of irregularity (Ir) were used to investigate the relationship between D and Ir. Furthermore, statistical analysis of E and D using XCAT controlled by 20 cancer patients' breathing profiles was conducted. Wilcoxon Signed Rank test was conducted to compare two modes.

RESULTS

D increased faster for cine-mode (D = 1.17 × Ir + 0.23) than sequential-mode (D = 0.47 × Ir + 0.23) as irregularity increased. For the XCAT study using 20 cancer patients' breathing profiles, the median E values were significantly different: 0.12 and 0.10 cm for cine- and sequential-modes, respectively, with a p-value of 0.02. The median D values were significantly different: 0.47 and 0.24 cm for cine- and sequential-modes, respectively, with a p-value < 0.001.

CONCLUSIONS

Respiratory motion measurement may be more accurate and less susceptible to breathing irregularity in sequential-mode 4D-MRI than that in cine-mode 4D-MRI.

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.

Automatic cardiac LV segmentation in MRI using modified graph cuts with smoothness and interslice constraints

PURPOSE

Magnetic resonance imaging (MRI), specifically late-enhanced MRI, is the standard clinical imaging protocol to assess cardiac viability. Segmentation of myocardial walls is a prerequisite for this assessment. Automatic and robust multisequence segmentation is required to support processing massive quantities of data.

METHODS

A generic rule-based framework to automatically segment the left ventricle myocardium is presented here. We use intensity information, and include shape and interslice smoothness constraints, providing robustness to subject- and study-specific changes. Our automatic initialization considers the geometrical and appearance properties of the left ventricle, as well as interslice information. The segmentation algorithm uses a decoupled, modified graph cut approach with control points, providing a good balance between flexibility and robustness.

RESULTS

The method was evaluated on late-enhanced MRI images from a 20-patient in-house database, and on cine-MRI images from a 15-patient open access database, both using as reference manually delineated contours. Segmentation agreement, measured using the Dice coefficient, was 0.81±0.05 and 0.92±0.04 for late-enhanced MRI and cine-MRI, respectively. The method was also compared favorably to a three-dimensional Active Shape Model approach.

CONCLUSION

The experimental validation with two magnetic resonance sequences demonstrates increased accuracy and versatility.