TY - JOUR
T1 - 3D right ventricular endocardium segmentation in cardiac magnetic resonance images by using a new inter-modality statistical shape modelling method
AU - Piazzese, Concetta
AU - Carminati, M. Chiara
AU - Krause, Rolf
AU - Auricchio, Angelo
AU - Weinert, Lynn
AU - Gripari, Paola
AU - Tamborini, Gloria
AU - Pontone, Gianluca
AU - Andreini, Daniele
AU - Lang, Roberto M.
AU - Pepi, Mauro
AU - Caiani, Enrico G.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - Objective: Statistical shape modelling (SSM) has established as a powerful method for segmenting the left ventricle in cardiac magnetic resonance (CMR) images However, applying them to segment the right ventricle (RV) is not straightforward because of the complex structure of this chamber. Our aim was to develop a new inter-modality SSM-based approach to detect the RV endocardium in CMR data. Methods: Real-time transthoracic 3D echocardiographic (3DE) images of 219 retrospective patients were used to populate a large database containing 4347 3D RV surfaces and train a model. The initial position, orientation and scale of the model in the CMR stack were semi-automatically derived. The detection process consisted in iteratively deforming the model to match endocardial borders in each CMR plane until convergence was reached. Clinical values obtained with the presented SSM method were compared with gold-standard (GS) corresponding parameters. Results: CMR images of 50 patients with different pathologies were used to test the proposed segmentation method. Average processing time was 2 min (including manual initialization) per patient. High correlations (r2 > 0.76) and not significant bias (Bland-Altman analysis) were observed when evaluating clinical parameters. Quantitative analysis showed high values of Dice coefficient (0.87 ± 0.03), acceptable Hausdorff distance (9.35 ± 1.51 mm) and small point-to-surface distance (1.91 ± 0.26 mm). Conclusion: A novel SSM-based approach to segment the RV endocardium in CMR scans by using a model trained on 3DE-derived RV endocardial surfaces, was proposed. This inter-modality technique proved to be rapid when segmenting the RV endocardium with an accurate anatomical delineation, in particular in apical and basal regions.
AB - Objective: Statistical shape modelling (SSM) has established as a powerful method for segmenting the left ventricle in cardiac magnetic resonance (CMR) images However, applying them to segment the right ventricle (RV) is not straightforward because of the complex structure of this chamber. Our aim was to develop a new inter-modality SSM-based approach to detect the RV endocardium in CMR data. Methods: Real-time transthoracic 3D echocardiographic (3DE) images of 219 retrospective patients were used to populate a large database containing 4347 3D RV surfaces and train a model. The initial position, orientation and scale of the model in the CMR stack were semi-automatically derived. The detection process consisted in iteratively deforming the model to match endocardial borders in each CMR plane until convergence was reached. Clinical values obtained with the presented SSM method were compared with gold-standard (GS) corresponding parameters. Results: CMR images of 50 patients with different pathologies were used to test the proposed segmentation method. Average processing time was 2 min (including manual initialization) per patient. High correlations (r2 > 0.76) and not significant bias (Bland-Altman analysis) were observed when evaluating clinical parameters. Quantitative analysis showed high values of Dice coefficient (0.87 ± 0.03), acceptable Hausdorff distance (9.35 ± 1.51 mm) and small point-to-surface distance (1.91 ± 0.26 mm). Conclusion: A novel SSM-based approach to segment the RV endocardium in CMR scans by using a model trained on 3DE-derived RV endocardial surfaces, was proposed. This inter-modality technique proved to be rapid when segmenting the RV endocardium with an accurate anatomical delineation, in particular in apical and basal regions.
KW - Cardiac magnetic resonance images
KW - Image segmentation
KW - Right ventricular volume
KW - Statistical shape model
UR - http://www.scopus.com/inward/record.url?scp=85078134302&partnerID=8YFLogxK
U2 - 10.1016/j.bspc.2020.101866
DO - 10.1016/j.bspc.2020.101866
M3 - Article
AN - SCOPUS:85078134302
VL - 58
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
SN - 1746-8094
M1 - 101866
ER -