Mitral annulus segmentation from four-dimensional ultrasound using a valve state predictor and constrained optical flow.

Mitral annulus segmentation from four-dimensional ultrasound using a valve state predictor and constrained optical flow.
复制标题

DOI:
10.1016/j.media.2011.11.006
复制
发表时间:
2012-02
影响因子:
10.9
通讯作者:
Howe, Robert D.
Howe, Robert D.
中科院分区:
工程技术1区
文献类型:
--
作者:
Schneider, Robert J.;Perrin, Douglas P.;Vasilyev, Nikolay V.;Marx, Gerald R.;del Nido, Pedro J.;Howe, Robert D.

文献摘要

参考文献

被引文献

相似文献

二尖瓣环的形状和运动的测量已被证明在许多应用中是有用的,包括病理诊断和二尖瓣建模。然而,从四维(4D)超声描绘瓣环的当前方法需要大量开销或用户交互,由于它们累积跟踪误差而变得不准确,或者它们不考虑瓣环形状或运动。本文提出了一种新的4D环形分割方法来解决这些不足。该方法建立在先前发表的三维(3D)瓣环分割算法的基础上,该算法在瓣膜关闭的情况下准确且稳健地分割二尖瓣环。在4D方法中,阀状态预测器确定阀何时关闭。随后,3D环带分割算法在这些帧中找到环带。对于具有开放瓣膜的帧,使用约束光流算法来跟踪环。算法的唯一输入是选择一个瓣膜关闭的帧和瓣膜附近的一个用户指定点,两者都不需要精确。通过将跟踪结果与由一组专家进行的手动分割进行比较,显示了跟踪方法的准确性,其中在30个跟踪帧中发现了1.67 ± 0.63 mm的平均RMS差异。
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 ± 0.63 mm was found across 30 tracked frames.
DOI: 10.1016/s0022-5223(96)70056-9
发表时间: 1996-09-01
影响因子: 6
作者:
Gorman, JH;Gupta, KB;Edmunds, LH
通讯作者: Edmunds, LH
DOI: 10.1023/a:1008122917811
发表时间: 2000-07-01
影响因子: 19.5
作者:
DeCarlo, D;Metaxas, D
通讯作者: Metaxas, D
DOI: 10.1067/mtc.2001.116313
发表时间: 2001-10-01
影响因子: 6
作者:
Dagum, P;Timek, T;Miller, DC
通讯作者: Miller, DC
DOI: 10.1016/s0002-8703(00)90077-2
发表时间: 2000-03-01
影响因子: 4.8
作者:
Kaplan, SR;Bashein, G;Martin, RW
通讯作者: Martin, RW
DOI: 10.1161/01.cir.80.3.589
发表时间: 1989-09-01
期刊: CIRCULATION
影响因子: 37.8
作者:
LEVINE, RA;HANDSCHUMACHER, MD;WEYMAN, AE
通讯作者: WEYMAN, AE