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

@article{Wei2013ThreedimensionalSO,
  title={Three-dimensional segmentation of the left ventricle in late gadolinium enhanced MR images of chronic infarction combining long- and short-axis information},
  author={Dong Wei and Ying Sun and Sim Heng Ong and Ping Chai and Lynette Ls Teo and Adrian Fatt-Hoe Low},
  journal={Medical image analysis},
  year={2013},
  volume={17 6},
  pages={
          685-97
        }
}

SK-Unet: An Improved U-Net Model with Selective Kernel for the Segmentation of Multi-sequence Cardiac MR

A modified U-net architecture by combining multi-sequence CMRIs, including the cine, LGE, and T2-weighted CMRI is proposed, which attains more precise segmentation result.

Multi-center, multi-vendor automated segmentation of left ventricular anatomy in contrast-enhanced MRI

The results obtained show that the combination of data augmentation and transfer learning can lead to single-center models that generalize well to new clinical centers not included in the original training.

SK-Unet: An Improved U-Net Model With Selective Kernel for the Segmentation of LGE Cardiac MR Images

A deep neural network method for automatic segmentation of the left ventricles, right ventricle (RV), and left ventricular myocardium (LVM) from LGE CMRIs, which also leverages complementary information from cine and T2-weighted CMRIS if available is proposed.

Segmentation of LG Enhanced Cardiac MRI

A method for segmentation of the endocardium in Late Gadolinium Enhanced Cardiac Magnetic Resonance (LGE-CMR) images is presented and combined with a previously proposed method, fully automatic and based on utilizing a priori knowledge about the type of images.

A Semi-Automated Approach for the Quantification of the Left Ventricle Chamber Volumes From Cine Magnetic Resonance Images

A new algorithm for the segmentation of the endocardium and epicardium of the left ventricle is developed, providing the basis for faster and still accurate quantification of the cardiac function from cardiac magnetic resonance, andThe basis for further processing aimed at the assessment of heart remodeling and diseases.

Segmentation of the left ventricle in short-axis sequences by combining deformation flow and optical flow

A semi-automatic approach is come up with that is capable of identifying the endocardial borders robustly from cine magnetic resonance images and is significantly more accurate than the referenced state of art methods.

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

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, which has the potential to achieve clinically relevant results.

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

A fully automatic framework for myocardium segmentation of delayed-enhancement (DE) MRI images without relying on prior patient-specific information is proposed, which can provide a benchmark for the myocardial segmentation in the literature.

References

SHOWING 1-10 OF 42 REFERENCES

Myocardial Segmentation of Late Gadolinium Enhanced MR Images by Propagation of Contours from Cine MR Images

Experimental results on real patient data with expert outlined ground truth show that the proposed framework can generate accurate and reliable results for myocardial segmentation of LGE CMR images.

Automatic myocardium segmentation in late-enhancement MRI

A novel automatic method to segment the myocardium on late-enhancement cardiac MR (LE CMR) images with a multi-step approach that shows robust and accurate results.

Quantification of Delayed Enhancement MR Images

A two-stage method for quantifying the extent of non-viable tissue is proposed, which uses MR Cine images acquired in the same session in order to create a prior model of the myocardial borders and employs a Support Vector Machine to distinguish viable from non- viable pixels based on training from an expert.

Automated segmentation of myocardial scar in late enhancement MRI using combined intensity and spatial information

The developed automatic MI identification method is accurate and robust in MI delineation, providing an objective tool for quantitative assessment of MI in LGE MR imaging.

Semiautomated Segmentation of Myocardial Contours for Fast Strain Analysis in Cine Displacement-Encoded MRI

This relatively accurate semiautomated segmentation method can be used to significantly increase the throughput of strain analysis of cine displacement-encoded MR images for clinical applications.

A review of segmentation methods in short axis cardiac MR images

Automatic Quantification of Myocardial Infarction from Delayed Enhancement MRI

A novel method for a fullyautomatic segmentation and quantification of myocardialinfarction from delay enhancement - magnetic resonanceimaging is developed and promising results have been achieved in quantification and segmentation of my cardiac infarct.

A Comprehensive Approach to the Analysis of Contrast Enhanced Cardiac MR Images

The evaluation shows that, despite limitations due to typical MRI artifacts, combined inspection is feasible and can yield clinically useful information.

Automatic segmentation of pathological tissues in cardiac MRI

The two main contributions are a generic image intensity analysis and an original variational segmentation method, the Fast Region Competition, which is robust to anatomical variability and partial volume effects and false positives are avoided.

Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association.

Attempts to standardize options for all cardiac imaging modalities should be based on the sound principles that have evolved from cardiac anatomy and clinical needs, and selection of standardized methods must bebased on the following criteria.