Dynamic Three-Dimensional Geometry of the Tricuspid Valve Annulus in Hypoplastic Left Heart Syndrome with a Fontan Circulation

Nguyen, Alex V.; Lassó, András; Nam, Hannah H.; Faerber, Jennifer; Aly, Ahmed; Pouch, Alison M.; Scanlan, Adam B.; McGowan, Francis X.; Mercer‐Rosa, Laura; Cohen, Meryl S.; Simpson, John; Fichtinger, Gabor; Jolley, Matthew A.

doi:10.1016/j.echo.2019.01.002

Cited by 29 publications

(37 citation statements)

References 39 publications

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“…TV failure remains a critical factor in both the quality of life and survival of patients with HLHS ( 1 ). Rapid, accurate creation of valve models remains a roadblock to the application of quantitative modeling and simulation of the tricuspid valve in children with HLHS ( 6 , 14 , 16 , 17 ). To the best of our knowledge, this is the first description of a DL-based approach to the segmentation of TV leaflets from 3DE, and to the segmentation of valves in children with congenital heart disease.…”

Section: Discussionmentioning

confidence: 99%

“…In January 2016, acquisition of TTE 3DE images of the TV became part of the standard clinical echocardiography lab protocol for HLHS at the Children's Hospital of Philadelphia ( 14 ). An institutional database was utilized to retrospectively identify patients with HLHS in whom transthoracic 3DE of the TV had been previously performed.…”

Section: Methodsmentioning

confidence: 99%

“…Figure 1 shows an example of TV segmentation and all annotated landmarks. First, an annular curve was manually created, as previously described ( 14 ). The annular curve creation takes an expert segmenter approximately thirty seconds for a rough curve and approximately two min for a high-precision curve.…”

Section: Methodsmentioning

confidence: 99%

“…Further, 3DE-based modeling has been proven to be more accurate than 2D measurements and superior for predicting repair durability and informing surgical decision making (11). However, despite the necessity of precise information to guide complicated repairs in children and preliminary work with basic tools suggesting relationships between the 3D structure of the TV (12)(13)(14) and patient survival (15), there is no readily available methodology for the rapid 3D segmentation of the TV and, in particular, the unique valves in HLHS.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation of Tricuspid Valve Leaflets From Transthoracic 3D Echocardiograms of Children With Hypoplastic Left Heart Syndrome Using Deep Learning

Herz

Pace

Lassó

et al. 2021

Front. Cardiovasc. Med.

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Hypoplastic left heart syndrome (HLHS) is a severe congenital heart defect in which the right ventricle and associated tricuspid valve (TV) alone support the circulation. TV failure is thus associated with heart failure, and the outcome of TV valve repair are currently poor. 3D echocardiography (3DE) can generate high-quality images of the valve, but segmentation is necessary for precise modeling and quantification. There is currently no robust methodology for rapid TV segmentation, limiting the clinical application of these technologies to this challenging population. We utilized a Fully Convolutional Network (FCN) to segment tricuspid valves from transthoracic 3DE. We trained on 133 3DE image-segmentation pairs and validated on 28 images. We then assessed the effect of varying inputs to the FCN using Mean Boundary Distance (MBD) and Dice Similarity Coefficient (DSC). The FCN with the input of an annular curve achieved a median DSC of 0.86 [IQR: 0.81–0.88] and MBD of 0.35 [0.23–0.4] mm for the merged segmentation and an average DSC of 0.77 [0.73–0.81] and MBD of 0.6 [0.44–0.74] mm for individual TV leaflet segmentation. The addition of commissural landmarks improved individual leaflet segmentation accuracy to an MBD of 0.38 [0.3–0.46] mm. FCN-based segmentation of the tricuspid valve from transthoracic 3DE is feasible and accurate. The addition of an annular curve and commissural landmarks improved the quality of the segmentations with MBD and DSC within the range of human inter-user variability. Fast and accurate FCN-based segmentation of the tricuspid valve in HLHS may enable rapid modeling and quantification, which in the future may inform surgical planning. We are now working to deploy this network for public use.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Segmentation of Tricuspid Valve Leaflets From Transthoracic 3D Echocardiograms of Children With Hypoplastic Left Heart Syndrome Using Deep Learning

Herz

Pace

Lassó

et al. 2021

Front. Cardiovasc. Med.

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“…Before beginning the segmentation process, the user must define the valve annulus by placing a series of points. This is accomplished through the SlicerHeart software, which facilitates the placing of the points and fits a smooth annulus curve [10]. This annulus definition is used throughout the automated process to provide context for the valve center, orientation and boundaries.…”

Section: Semi-automated Image Analysismentioning

confidence: 99%

Interactive-Automatic Segmentation and Modelling of the Mitral Valve

Carnahan

Ginty

Moore

et al. 2019

Functional Imaging and Modeling of the Heart

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Mitral valve regurgitation is the most common valvular disease, affecting 10% of the population over 75 years old. Left untreated, patients with mitral valve regurgitation can suffer declining cardiac health until cardiac failure and death. Mitral valve repair is generally preferred over valve replacement. However, there is a direct correlation between the volume of cases performed and surgical outcomes, therefore there is a demand for the ability of surgeons to practice repairs on patient specific models in advance of surgery. This work demonstrates a semi-automated segmentation method to enable fast and accurate modelling of the mitral valve that captures patient-specific valve geometry. This modelling approach utilizes 3D active contours in a user-in-the-loop system which segments first the atrial blood pool, then the mitral leaflets. In a group of 15 mitral valve repair patients, valve segmentation and modelling attains an overall accuracy (mean absolute surface distance) of 1.40 ± 0.26 mm, and an accuracy of 1.01 ± 0.13 mm when only comparing the extracted leaflet surface proximal to the ultrasound probe. Thus this image-based segmentation tool has the potential to improve the workflow for extracting patient-specific mitral valve geometry for 3D modelling of the valve.

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