\n \n \n
\n
\n\n \n \n \n \n \n \n Fast and Automated Segmentation for the Three-Directional Multi-Slice Cine Myocardial Velocity Mapping.\n \n \n \n \n\n\n \n Wu, Y.; Hatipoglu, S.; Alonso-Álvarez, D.; Gatehouse, P.; Li, B.; Gao, Y.; Firmin, D.; Keegan, J.; and Yang, G.\n\n\n \n\n\n\n
Diagnostics, 11(2). 2021.\n
\n\n
\n\n
\n\n
\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@Article{diagnostics11020346,\nAUTHOR = {Wu, Yinzhe and Hatipoglu, Suzan and Alonso-Álvarez, Diego and Gatehouse, Peter and Li, Binghuan and Gao, Yikai and Firmin, David and Keegan, Jennifer and Yang, Guang},\nTITLE = {Fast and Automated Segmentation for the Three-Directional Multi-Slice Cine Myocardial Velocity Mapping},\nJOURNAL = {Diagnostics},\nVOLUME = {11},\nYEAR = {2021},\nNUMBER = {2},\nARTICLE-NUMBER = {346},\nURL = {https://www.mdpi.com/2075-4418/11/2/346},\nPubMedID = {33669747},\nISSN = {2075-4418},\nABSTRACT = {Three-directional cine multi-slice left ventricular myocardial velocity mapping (3Dir MVM) is a cardiac magnetic resonance (CMR) technique that allows the assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 3Dir MVM also provides three orthogonal phase velocity mapping datasets, which are used to generate velocity maps. These velocity maps may also be used to facilitate and improve the myocardial delineation. Based on the success of deep learning in medical image processing, we propose a novel fast and automated framework that improves the standard U-Net-based methods on these CMR multi-channel data (magnitude and phase velocity mapping) by cross-channel fusion with an attention module and the shape information-based post-processing to achieve accurate delineation of both epicardial and endocardial contours. To evaluate the results, we employ the widely used Dice Scores and the quantification of myocardial longitudinal peak velocities. Our proposed network trained with multi-channel data shows superior performance compared to standard U-Net-based networks trained on single-channel data. The obtained results are promising and provide compelling evidence for the design and application of our multi-channel image analysis of the 3Dir MVM CMR data.},\nDOI = {10.3390/diagnostics11020346}\n}\n\n\n
\n
\n\n\n
\n Three-directional cine multi-slice left ventricular myocardial velocity mapping (3Dir MVM) is a cardiac magnetic resonance (CMR) technique that allows the assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 3Dir MVM also provides three orthogonal phase velocity mapping datasets, which are used to generate velocity maps. These velocity maps may also be used to facilitate and improve the myocardial delineation. Based on the success of deep learning in medical image processing, we propose a novel fast and automated framework that improves the standard U-Net-based methods on these CMR multi-channel data (magnitude and phase velocity mapping) by cross-channel fusion with an attention module and the shape information-based post-processing to achieve accurate delineation of both epicardial and endocardial contours. To evaluate the results, we employ the widely used Dice Scores and the quantification of myocardial longitudinal peak velocities. Our proposed network trained with multi-channel data shows superior performance compared to standard U-Net-based networks trained on single-channel data. The obtained results are promising and provide compelling evidence for the design and application of our multi-channel image analysis of the 3Dir MVM CMR data.\n
\n\n\n
\n\n\n
\n
\n\n \n \n \n \n \n \n Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives.\n \n \n \n \n\n\n \n Wu, Y.; Tang, Z.; Li, B.; Firmin, D.; and Yang, G.\n\n\n \n\n\n\n
Frontiers in Physiology, 12: 1111. 2021.\n
\n\n
\n\n
\n\n
\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@ARTICLE{10.3389/fphys.2021.709230,\n \nAUTHOR={Wu, Yinzhe and Tang, Zeyu and Li, Binghuan and Firmin, David and Yang, Guang}, \n\t \nTITLE={Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives}, \n\t\nJOURNAL={Frontiers in Physiology}, \n\t\nVOLUME={12}, \n\nPAGES={1111}, \n\t\nYEAR={2021}, \n\t \nURL={https://www.frontiersin.org/article/10.3389/fphys.2021.709230}, \n\t\nDOI={10.3389/fphys.2021.709230}, \n\t\nISSN={1664-042X}, \n \nABSTRACT={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.}\n}\n\n
\n
\n\n\n
\n 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.\n
\n\n\n
\n\n\n\n\n\n