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Mitral annulus segmentation from 3D ultrasound using graph cuts.

ABSTRACT: The shape of the mitral valve annulus is used in diagnostic and modeling applications, yet methods to accurately and reproducibly delineate the annulus are limited. This paper presents a mitral annulus segmentation algorithm designed for closed mitral valves which locates the annulus in three-dimensional ultrasound using only a single user-specified point near the center of the valve. The algorithm first constructs a surface at the location of the thin leaflets, and then locates the annulus by finding where the thin leaflet tissue meets the thicker heart wall. The algorithm iterates until convergence metrics are satisfied, resulting in an operator-independent mitral annulus segmentation. The accuracy of the algorithm was assessed from both a diagnostic and surgical standpoint by comparing the algorithm's results to delineations made by a group of experts on clinical ultrasound images of the mitral valve, and to delineations made by an expert with a surgical view of the mitral annulus on excised porcine hearts using an electromagnetically tracked pointer. In the former study, the algorithm was statistically indistinguishable from the best performing expert (p=0.85) and had an average RMS difference of 1.81+/-0.78 mm to the expert average. In the latter, the average RMS difference between the algorithm's annulus and the electromagnetically tracked points across six hearts was 1.19+/-0.17 mm .

SUBMITTER: Schneider RJ

PROVIDER: S-EPMC3122108 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

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Mitral annulus segmentation from 3D ultrasound using graph cuts.

Schneider Robert J RJ Perrin Douglas P DP Vasilyev Nikolay V NV Marx Gerald R GR del Nido Pedro J PJ Howe Robert D RD

IEEE transactions on medical imaging 20100617 9

The shape of the mitral valve annulus is used in diagnostic and modeling applications, yet methods to accurately and reproducibly delineate the annulus are limited. This paper presents a mitral annulus segmentation algorithm designed for closed mitral valves which locates the annulus in three-dimensional ultrasound using only a single user-specified point near the center of the valve. The algorithm first constructs a surface at the location of the thin leaflets, and then locates the annulus by f ...[more]

PMID: 20562042

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Project description:This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images.The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al. [Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine.The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 degrees and 0.03, and over all foot bones are about 3.5709 mm, 0.35 degrees and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and all foot bones for all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s.The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min.

Dataset Information

Mitral annulus segmentation from 3D ultrasound using graph cuts.

Publications

Mitral annulus segmentation from 3D ultrasound using graph cuts.

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