3D right ventricular endocardium segmentation in cardiac magnetic resonance images by using a new inter-modality statistical shape modelling method. (April 2020)

• Record Type: Journal Article
• Authors: Piazzese, Concetta; Carminati, M. Chiara; Krause, Rolf; Auricchio, Angelo; Weinert, Lynn; Gripari, Paola; Tamborini, Gloria; Pontone, Gianluca; Andreini, Daniele; Lang, Roberto M.; Pepi, Mauro; Caiani, Enrico G.
• Is Part Of: Biomedical signal processing and control. Volume 58(2020)
• Publication Date: 2020-04
• Subjects: Cardiac magnetic resonance images -- Right ventricular volume -- Image segmentation -- Statistical shape model; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.101866
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Inter-modality statistical modelling approach for a fast and accurate detection of the RV endocardium in MR images. 3D statistical shape model trained using shapes obtained from real-time three-dimensional echocardiographic images. Realistic anatomical delineation of the right ventricle morphology especially at apical and basal levels. Abstract: 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 (r 2 > 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.