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Objective\ud\udStatistical 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.\udMethods\ud\udReal-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.\udResults\ud\udCMR 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\ud\udA 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.

International audience; We evaluate in this paper different strategies for the construction of a statistical shape model (SSM) of the left ventricle (LV) to be used for segmentation in cardiac magnetic resonance (CMR) images. From a large database of LV surfaces obtained throughout the cardiac cycle from 3D echocardiographic (3DE) LV images, different LV shape models were built by varying the considered phase in the cardiac cycle and the registration procedure employed for surface alignment. Principal component analysis was computed to describe the statistical variability of the SSMs, which were then deformed by applying an active shape model (ASM) approach to segment the LV endocardium in CMR images of 45 patients. Segmentation performance was evaluated by comparing LV volumes derived by ASM segmentation with different SSMs and those obtained by manual tracing, considered as a reference. A high correlation (r2 > 0.92) was found in all cases, with better results when using the SSM models comprising more than one frame of the cardiac cycle.

Object We present in this paper the application of a statistical shape model of the left ventricle (LV) built from transthoracic real time 3D echocardiography (3DE) to segment the LV endocardium and epicardium in cardiac magnetic resonance (CMR) images. Material and methods The LV model was built from a training database constituted by over 9000 surfaces obtained from retrospectively selected 3DE examination of 435 patients with various pathologies. Three-dimensional segmentation of the endocardium and the epicardium was obtained by processing CMR images acquired in 30 patients with a dedicated active shape modelling (ASM) algorithm using the proposed LV model. Results The segmentation results obtained with the proposed method were compared with those obtained by the manual reference technique; similarity was proven by computing: i) point to surface distance ( 89%), iii) Hausdorff distance ( ∼ 5 mm). This was furthermore confirmed by equivalence testing, linear regression and Bland Altman analysis applied on derived clinical parameters, such as LV volumes and mass. Conclusions This study showed the potential usefulness of the proposed inter-modal ASM approach featuring a 3DE-based LV model for the 3D segmentation of the LV myocardium in CMR images.

A robust and efficient approach to reconstruction of the descending thoracic aorta from contiguous 3-D transesophageal echocardiographic (TEE) images is proposed. An ad hoc image acquisition protocol was designed to acquire ordered and partially overlapped 3-D TEE data sets, followed by dedicated image processing to align and fuse all acquired data sets. Alignment strategy implemented pairwise rigid registration guided by a priori knowledge, and it was validated using artificially misaligned images. Image fusion was finally performed to enable visualization and analysis of extended field-of-view of the acquired aorta. The application of different fusion techniques was also investigated. The method was applied to a population of 17 consecutive patients. Qualitative and quantitative results supported the feasibility and accuracy of the proposed approach. In a clinical scenario, its application could allow the quantitative assessment of aortic plaque burden in the descending thoracic aorta from 3-D TEE images.

We propose a robust and efficient approach for the reconstruction of the descending aorta from contiguous 3D transesophageal echocardiographic (TEE) images. It is based on an ad hoc protocol, designed to acquire ordered and partially overlapped 3D TEE datasets, followed by automated image registration that relies on this a priori knowledge. The method was validated using artificially derived misaligned images, and then applied to 14 consecutive patients. Both qualitative and quantitative results demonstrated the potential feasibility and accuracy of the proposed approach. Its clinical applicability could improve the assessment of aortic total plaque burden from 3D TEE images.

Statistical shape modelling (SSM) approaches have been proposed as a powerful tool to segment the left ventricle in cardiac magnetic resonance (CMR) images. Our aim was to extend this method to segment the RV cavity in CMR images and validate it compared to the conventional gold-standard (GS) manual tracing. A SSM of the RV was built using a database of 4347 intrinsically 3D surfaces, extracted from transthoracic 3D echo cardiographic (3DE) images of 219 retrospective patients. The SSM was then scaled and deformed on the base of some features extracted, with different strategies, from each short-axis plane until a stable condition was reached. The proposed approach, tested on 14 patients, resulted in a high correlation (r2=0.97) and narrow limits of agreement (± 17% error) when comparing the semiautomatic volumes to the GS, confirming the accuracy of this approach in segmenting the RV endocardium.