Semi-automated segmentation of the mitral leafelts in 3D echocardiographic images

Precise 3D modeling of the mitral valve has the potential to improve our understanding of valve morphology, particularly in the setting of mitral regurgitation (MR). Toward this goal, we have developed a user-initialized algorithm for reconstructing valve geometry from transesophageal 3D echocardiographic (3DE) image data.

Image analysis of the mitral valve at mid systole has two stages:

  • user-initialized level sets segmentation
  • 3D deformable modeling with continuous medial representation (cm-rep).

Semi-automated segmentation begins with user identification of valve location in 2D projection images generated from 3D US data. The mitral leaflets are then automatically segmented in 3D using the level set method.

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(a) The user initializes two points in a long-axis cross-section of the 3DE image volume, identifying an ROI (red) containing the valve along the axial dimension. (b) The user initializes a series of annular points in an enhanced projection image depicting the valve from an atrial perspective. (c) The user shifts posterior annular points into the coaptation zone, forming an outline of the anterior leaflet in the enhanced projection image. (d) A 3D point cloud delineating the valve is automatically generated. (e) The 3D point cloud is morphologically dilated with a spherical structuring element to obtain an ROI containing the valve. (f) A final segmentation of the valve is obtained by thresholding and active contour evolution. (LA=left atrium, LV=left ventricle, AL=anterior leaflet, PL=posterior leaflet, RO=regurgitant orifice).

Second, a bileaflet deformable medial model is fitted to the mitral leaflet segmentation by Bayesian optimization. The resulting cm-rep provides a visual reconstruction of the mitral valve, from which localized measurements of valve morphology are automatically derived.

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A medial representation of the valve is obtained by fitting a cm-rep template to a binary segmentation of the mitral leaflets.

Features such as anatomic regurgitant orifice area can be both visually observed and quantified from image-derived cm-reps of the mitral leaflets.

valves

Automated reconstructions of the valves of six patients with various degrees of mitral regurgitation severity as shown from two viewpoints (atrial and lateral perspectives).

 

  • [DOI] A. M. Pouch, P. A. Yushkevich, B. M. Jackson, A. S. Jassar, M. Vergnat, J. H. Gorman, R. C. Gorman, and C. M. Sehgal, “Development of a semi-automated method for mitral valve modeling with medial axis representation using 3D ultrasound.,” Med Phys, vol. 39, iss. 2, pp. 933-950, 2012.
    [Bibtex]
    @ARTICLE{Pouch2012MP,
    author = {Pouch, Alison M. and Yushkevich, Paul A. and Jackson, Benjamin M.
    and Jassar, Arminder S. and Vergnat, Mathieu and Gorman, Joseph H.
    and Gorman, Robert C. and Sehgal, Chandra M.},
    title = {{D}evelopment of a semi-automated method for mitral valve modeling
    with medial axis representation using 3{D} ultrasound.},
    journal = {{M}ed {P}hys},
    year = {2012},
    volume = {39},
    pages = {933--950},
    number = {2},
    month = {Feb},
    abstract = {Precise 3D modeling of the mitral valve has the potential to improve
    our understanding of valve morphology, particularly in the setting
    of mitral regurgitation (MR). Toward this goal, the authors have
    developed a user-initialized algorithm for reconstructing valve geometry
    from transesophageal 3D ultrasound (3D US) image data.Semi-automated
    image analysis was performed on transesophageal 3D US images obtained
    from 14 subjects with MR ranging from trace to severe. Image analysis
    of the mitral valve at midsystole had two stages: user-initialized
    segmentation and 3D deformable modeling with continuous medial representation
    (cm-rep). Semi-automated segmentation began with user-identification
    of valve location in 2D projection images generated from 3D US data.
    The mitral leaflets were then automatically segmented in 3D using
    the level set method. Second, a bileaflet deformable medial model
    was fitted to the binary valve segmentation by Bayesian optimization.
    The resulting cm-rep provided a visual reconstruction of the mitral
    valve, from which localized measurements of valve morphology were
    automatically derived. The features extracted from the fitted cm-rep
    included annular area, annular circumference, annular height, intercommissural
    width, septolateral length, total tenting volume, and percent anterior
    tenting volume. These measurements were compared to those obtained
    by expert manual tracing. Regurgitant orifice area (ROA) measurements
    were compared to qualitative assessments of MR severity. The accuracy
    of valve shape representation with cm-rep was evaluated in terms
    of the Dice overlap between the fitted cm-rep and its target segmentation.The
    morphological features and anatomic ROA derived from semi-automated
    image analysis were consistent with manual tracing of 3D US image
    data and with qualitative assessments of MR severity made on clinical
    radiology. The fitted cm-reps accurately captured valve shape and
    demonstrated patient-specific differences in valve morphology among
    subjects with varying degrees of MR severity. Minimal variation in
    the Dice overlap and morphological measurements was observed when
    different cm-rep templates were used to initialize model fitting.This
    study demonstrates the use of deformable medial modeling for semi-automated
    3D reconstruction of mitral valve geometry using transesophageal
    3D US. The proposed algorithm provides a parametric geometrical representation
    of the mitral leaflets, which can be used to evaluate valve morphology
    in clinical ultrasound images.},
    doi = {10.1118/1.3673773},
    institution = {{D}epartment of {B}ioengineering, {U}niversity of {P}ennsylvania,
    {P}hiladelphia, {PA} 19104, {USA}. pouch@seas.upenn.edu},
    keywords = {Algorithms; Computer Simulation; Echocardiography, Three-Dimensional,
    methods; Humans; Image Enhancement, methods; Image Interpretation,
    Computer-Assisted, methods; Mitral Valve, anatomy /&/ histology/ultrasonography;
    Models, Anatomic; Models, Cardiovascular; Pattern Recognition, Automated,
    methods; Reproducibility of Results; Sensitivity and Specificity},
    language = {eng},
    medline-pst = {ppublish},
    owner = {alison},
    pmid = {22320803},
    timestamp = {2014.02.27},
    url = {http://dx.doi.org/10.1118/1.3673773}
    }