A fully automatic learning-based method using deep learning and deformable models achieved an average Dice metric of 82.5% and Hausdorff distance of 7.85 mm for right ventricle segmentation.
Does a deep learning-based approach accurately segment the right ventricle from cardiac MRI?
Deep learning algorithms combined with deformable models provide accurate and robust automatic segmentation of the right ventricle from cardiac MRI, outperforming previous challenge techniques.
PURPOSE: This study aims to accurately segment the right ventricle (RV) from cardiac MRI using a fully automatic learning-based method. METHODS: The proposed method uses deep learning algorithms, i.e., convolutional neural networks and stacked autoencoders, for automatic detection and initial segmentation of the RV chamber. The initial segmentation is then combined with the deformable models to improve the accuracy and robustness of the process. We trained our algorithm using 16 cardiac MRI datasets of the MICCAI 2012 RV Segmentation Challenge database and validated our technique using the rest of the dataset (32 subjects). RESULTS: An average Dice metric of 82.5% along with an average Hausdorff distance of 7.85 mm were achieved for all the studied subjects. Furthermore, a high correlation and level of agreement with the ground truth contours for end-diastolic volume (0.98), end-systolic volume (0.99), and ejection fraction (0.93) were observed. CONCLUSION: Our results show that deep learning algorithms can be effectively used for automatic segmentation of the RV. Computed quantitative metrics of our method outperformed that of the existing techniques participated in the MICCAI 2012 challenge, as reported by the challenge organizers. Magn Reson Med 78:2439-2448, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
Avendi et al. (Thu,) conducted a other in Right ventricle segmentation from cardiac MRI (n=48). Deep learning algorithms (convolutional neural networks and stacked autoencoders) combined with deformable models vs. Existing techniques from the MICCAI 2012 challenge / ground truth contours was evaluated on Average Dice metric and average Hausdorff distance. A fully automatic learning-based method using deep learning and deformable models achieved an average Dice metric of 82.5% and Hausdorff distance of 7.85 mm for right ventricle segmentation.