Purpose:
Segmentation of cardiac medical images, an important step in measuring cardiac function, is usually performed either manually or semi-automatically. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV) as well as the myocardium of 3D magnetic resonance (MR) images throughout the entire cardiac cycle (4D), remains challenging. This study proposes a deformable-based segmentation methodology for efficiently segmenting 4D (3D+t) cardiac MR images.
Methods:
The proposed methodology first used the Hough transform and the local Gaussian distribution method (LGD) to segment the LV endocardial contours from cardiac MR images. Following that a novel level set-based shape prior method was applied to generate the LV epicardial contours and the RV boundary.
Results:
This automatic image segmentation approach has been applied to studies on 17 subjects. The results demonstrated that the proposed method was efficient compared to manual segmentation, achieving a segmentation accuracy with average Dice values of 88.62±5.47%, 87.35±7.26%, and 82.63±6.22% for the LV endocardial, LV epicardial and RV contours, respectively.
Conclusions:
We have presented a method for accurate LV and RV segmentation. Compared to three existing methods, the proposed method can successfully segment the LV and yields the highest Dice value. This makes it an option for clinical assessment of the volume, size and thickness of the ventricles.
Background: The segmentation of cardiac medical images is a crucial step for calculating clinical indices such as wall thickness, ventricular volume, and ejection fraction.Methods: In this study, we introduce a method named LsUnet that combines multi-channel, fully convolutional neural network, and annular shape level-set methods for efficiently segmenting cardiac cine magnetic resonance (MR) images. In this method, the multi-channel deep learning algorithm is applied to train the segmentation task to extract the left ventricle (LV) endocardial and epicardial contours. Next, the segmentation contours from the multi-channel deep learning method are incorporated into a level-set formulation, which is dedicated explicitly to detecting annular shapes to assure the segmentation's accuracy and robustness.
Results:The proposed automatic approach was evaluated on 95 volumes (total 1,076 slices, ~80% as for training datasets, ~20% 2D as for testing datasets). This combined multi-channel deep learning and annular shape level-set segmentation method achieved high accuracy with average Dice values reaching 92.15% and 95.42% for LV endocardium and epicardium delineation, respectively, in comparison to the reference standard (the manual segmentation).Conclusions: A novel method for fully automatic segmentation of the LV endocardium and epicardium from different MRI datasets is presented. The proposed workflow is accurate and robust compared to the reference and other state-of-the-art methods.
2. Natural regenerative mechanisms 2.1. Lower vertebrates Several lower vertebrates are known to retain the ability to efficiently regenerate injured myocardial tissue, in addition to central nervous system and appendages, throughout adulthood. These include some urodele amphibians, such as the newt (Witman et al., 2011) and axolotl (Cano-Martínez et al., 2010), as well as zebrafish (Poss et al., 2002; Jopling et al., 2010; Kikuchi et al., 2010) and Polypterus senegalus (Kikuchi et al., 2011) (Figure). A phylogenetic tree would suggest that the ability to regenerate cardiac tissue may have been present in a common ancestor between these species and mammals. Several theories have been proposed to explain the source of cardiomyocyte replacement in these regenerative organisms, including circulating stem cells, resident stem/ progenitor cells, and the dedifferentiation, proliferation and redifferentiation of mature cells. There seems to be a growing consensus that preexisting cardiomyocytes are the predominant source for new myocardium in zebrafish heart regeneration (Jopling et al., 2010; Kikuchi et al., 2010) (in addition to neonatal mice, discussed below). Jopling et al. (2010) and Kikuchi et al. (2010) both used genetic lineage tracing experiments to track the fate of cardiomyocytes during regeneration in an apical resection model. Jopling et al. (2010) labeled cardiomyocytes 48 h after fertilization by tamoxifen pulsing transgenic cmlc2a-Cre-Ert2;cmlc2a-LnL-GFP zebrafish and performed 20%
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.