Mitral annulus segmentation from four-dimensional ultrasound using a valve state predictor and constrained optical flow.
Measurement of the shape and motion of the mitral valve annulus has proven useful in a number of applications, including pathology diagnosis and mitral valve modeling. Current methods to delineate the annulus from four-dimensional (4D) ultrasound, however, either require extensive overhead or user-interaction, become inaccurate as they accumulate tracking error, or they do not account for annular shape or motion. This paper presents a new 4D annulus segmentation method to account for these deficiencies. The method builds on a previously published three-dimensional (3D) annulus segmentation algorithm that accurately and robustly segments the mitral annulus in a frame with a closed valve. In the 4D method, a valve state predictor determines when the valve is closed. Subsequently, the 3D annulus segmentation algorithm finds the annulus in those frames. For frames with an open valve, a constrained optical flow algorithm is used to the track the annulus. The only inputs to the algorithm are the selection of one frame with a closed valve and one user-specified point near the valve, neither of which needs to be precise. The accuracy of the tracking method is shown by comparing the tracking results to manual segmentations made by a group of experts, where an average RMS difference of 1.67±0.63mm was found across 30 tracked frames.
Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA. rjschn@seas.harvard.edu
Robert J
RJ
Schneider
Douglas P
DP
Perrin
Nikolay V
NV
Vasilyev
Gerald R
GR
Marx
Pedro J
PJ
del Nido
Robert D
RD
Howe
D000465:Algorithms; D055424:Cardiac-Gated Imaging Techniques / Q000379:methods*; D019560:Echocardiography, Three-Dimensional / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008943:Mitral Valve / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity