SlicerHeart: An open-source computing platform for cardiac image analysis and modeling

Lassó, András; Herz, Christian; Nam, Hannah H.; Cianciulli, Alana; Pieper, Steve; Drouin, Simon; Pintér, Csaba; St-Onge, Samuelle; Vigil, Chad; Ching, Stephen; Sunderland, Kyle; Fichtinger, Gabor; Kikinis, Ron; Jolley, Matthew A.

doi:10.3389/fcvm.2022.886549

Cited by 15 publications

(26 citation statements)

References 57 publications

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“…All code used for modeling is available open-source at www.github.com/SlicerHeart and implemented in the SlicerHeart extension for 3D Slicer. 9 All code used for shape analysis and principal component analysis is available at salt.slicer.org and implemented in the SlicerSALT extension for 3D Slicer. 10 Tutorials for application of principal component analysis using the Shape Variation Analyzer tool within SlicerSALT are available at salt.slicer.org .…”

Section: Methodsmentioning

confidence: 99%

“…3DE images of patients with HLHS were imported into 3D Slicer using SlicerHeart as we have previously described. 9 , 11 , 12 TV annular curve modeling was performed using a new TV preset for the Valve Annulus Analysis in the SlicerHeart extension. 9 , 11 Annular modeling was performed in end-diastole (ED), mid-systole (MS), end-systole (ES), and mid-diastole.…”

Section: Methodsmentioning

confidence: 99%

“… 9 , 11 , 12 TV annular curve modeling was performed using a new TV preset for the Valve Annulus Analysis in the SlicerHeart extension. 9 , 11 Annular modeling was performed in end-diastole (ED), mid-systole (MS), end-systole (ES), and mid-diastole. EKG was not available in Cartesian Digital Imaging and Communications in Medicine, so timing of cardiac cycle was determined by valve position as previously described.…”

Section: Methodsmentioning

confidence: 99%

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Modeling of the Tricuspid Valve and Right Ventricle in Hypoplastic Left Heart Syndrome With a Fontan Circulation

Nam¹,

Flynn²,

Lassó

et al. 2023

Circ: Cardiovascular Imaging

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Background: In hypoplastic left heart syndrome, tricuspid regurgitation (TR) is associated with circulatory failure and death. We hypothesized that the tricuspid valve (TV) structure of patients with hypoplastic left heart syndrome with a Fontan circulation and moderate or greater TR differs from those with mild or less TR, and that right ventricle volume is associated with TV structure and dysfunction. Methods: TV of 100 patients with hypoplastic left heart syndrome and a Fontan circulation were modeled using transthoracic 3-dimensional echocardiograms and custom software in SlicerHeart. Associations of TV structure to TR grade and right ventricle function and volume were investigated. Shape parameterization and analysis was used to calculate the mean shape of the TV leaflets, their principal modes of variation, and to characterize associations of TV leaflet shape to TR. Results: In univariate modeling, patients with moderate or greater TR had larger TV annular diameters and area, greater annular distance between the anteroseptal commissure and anteroposterior commissure, greater leaflet billow volume, and more laterally directed anterior papillary muscle angles compared to valves with mild or less TR (all P <0.001). In multivariate modeling greater total billow volume, lower anterior papillary muscle angle, and greater distance between the anteroposterior commissure and anteroseptal commissure were associated with moderate or greater TR ( P <0.001, C statistic=0.85). Larger right ventricle volumes were associated with moderate or greater TR ( P <0.001). TV shape analysis revealed structural features associated with TR, but also highly heterogeneous TV leaflet structure. Conclusions: Moderate or greater TR in patients with hypoplastic left heart syndrome with a Fontan circulation is associated with greater leaflet billow volume, a more laterally directed anterior papillary muscle angle, and greater annular distance between the anteroseptal commissure and anteroposterior commissure. However, there is significant heterogeneity of structure in the TV leaflets in regurgitant valves. Given this variability, an image-informed patient-specific approach to surgical planning may be needed to achieve optimal outcomes in this vulnerable and challenging population.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Modeling of the Tricuspid Valve and Right Ventricle in Hypoplastic Left Heart Syndrome With a Fontan Circulation

Nam¹,

Flynn²,

Lassó

et al. 2023

Circ: Cardiovascular Imaging

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“…Inverse finite element analysis requires an iterative process to match the ground-truth segmented leaflet surface or point cloud to the finite element mesh. As a proof of concept, we used 3D Slicer (an open-source image segmentation software) to manually trace the TV leaflets of a representative HLHS patient by placing individual points throughout the 3D echocardiogram image stack, obtaining the open (reference configuration) and closed valve geometry (see Figure 4 a, and a simplified finite element model in Figure 4 b) [ 91 , 92 ]. The open valve geometry was used to generate the finite element mesh for the TV leaflet surface (i.e., a lofted surface, see [ 93 ]).…”

Section: Lacking Knowledge and Future Perspectivesmentioning

confidence: 99%

Tricuspid Valve Regurgitation in Hypoplastic Left Heart Syndrome: Current Insights and Future Perspectives

Ross

Mir

Burkhart

et al. 2023

JCDD

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Hypoplastic Left Heart Syndrome (HLHS) is a congenital heart defect that requires a three-stage surgical palliation to create a single ventricle system in the right side of the heart. Of patients undergoing this cardiac palliation series, 25% will develop tricuspid regurgitation (TR), which is associated with an increased mortality risk. Valvular regurgitation in this population has been extensively studied to understand indicators and mechanisms of comorbidity. In this article, we review the current state of research on TR in HLHS, including identified valvular anomalies and geometric properties as the main reasons for the poor prognosis. After this review, we present some suggestions for future TR-related studies to answer the central question: What are the predictors of TR onset during the three palliation stages? These studies involve (i) the use of engineering-based metrics to evaluate valve leaflet strains and predict tissue material properties, (ii) perform multivariate analyses to identify TR predictors, and (iii) develop predictive models, particularly using longitudinally tracked patient cohorts to foretell patient-specific trajectories. Regarded together, these ongoing and future efforts will result in the development of innovative tools that can aid in surgical timing decisions, in prophylactic surgical valve repair, and in the refinement of current intervention techniques.

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“…On average each participant had 10 ultrasounds sweeps acquisitions per measurement, resulting on over 50,000 ultrasound images. All Kretz ultrasound volumes were automatically converted to NIFTI with SlicerHeart extension for 3D Slicer (28), with a fixed isotropic voxel size of 0.6 mm. A crop and orientation step was implemented to align the fetal brains.…”

Section: Image Preprocessingmentioning

confidence: 99%

Automated segmentation of fetal intracranial volume in 3D ultrasound using deep learning: identifying sex differences in prenatal brain development

Zwarte

Teeuw

et al. 2022

Preprint

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The human brain undergoes major developmental changes during pregnancy. Three-dimensional (3D) ultrasound images allow for the opportunity to investigate typical prenatal brain development on a large scale. Here, we developed a convolutional neural network (CNN) model for automated segmentation of fetal intracranial volume (ICV) from 3D ultrasound, and we applied the trained model in a large independent sample (N = 9795 ultrasounds; N=1763 participants) from the YOUth Baby and Child cohort measured at 20- and 30-week of gestational age to investigate sex differences in fetal ICV and ICV growth. 168 3D ultrasound images with ICV annotations were included to develop a 3D CNN model for automated fetal ICV segmentation. A data augmentation strategy provided physical variation and additional data to train the network. K-fold cross-validation and Bayesian optimization were used for network selection and the ensemble-based system combined multiple networks to form the final ensemble network. The final ensemble network produced consistent and high-quality segmentations of ICV. Our trained model successfully predicted ICV and ICV growth in a large independent longitudinal data set. Boys had significantly larger ICV at 20- (B=2.83; p=1.4e-13) and 30-weeks of pregnancy (B=12.01; p=2.0e-28), and more pronounced ICV growth than girls (t=-4.395; p=1.3e-5). Our approach provides us with the opportunity to investigate fetal brain development on a much larger scale and to answer fundamental questions related to prenatal brain development.

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SlicerHeart: An open-source computing platform for cardiac image analysis and modeling

Cited by 15 publications

References 57 publications

Modeling of the Tricuspid Valve and Right Ventricle in Hypoplastic Left Heart Syndrome With a Fontan Circulation

Modeling of the Tricuspid Valve and Right Ventricle in Hypoplastic Left Heart Syndrome With a Fontan Circulation

Tricuspid Valve Regurgitation in Hypoplastic Left Heart Syndrome: Current Insights and Future Perspectives

Automated segmentation of fetal intracranial volume in 3D ultrasound using deep learning: identifying sex differences in prenatal brain development

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