Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

25 Scopus Citations
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Author(s)

  • Yuanwei Li
  • Matthieu Toulemonde
  • Navtej Chahal
  • Roxy Senior
  • Meng-Xing Tang

Detail(s)

Original languageEnglish
Pages (from-to)1081-1091
Journal / PublicationIEEE Transactions on Medical Imaging
Volume37
Issue number5
Online published26 Sep 2017
Publication statusPublished - May 2018
Externally publishedYes

Abstract

Myocardial contrast echocardiography (MCE) is an imaging technique that assesses left ventricle function and myocardial perfusion for the detection of coronary artery diseases. Automatic MCE perfusion quantification is challenging and requires accurate segmentation of the myocardium from noisy and time-varying images. Random forests (RF) have been successfully applied to many medical image segmentation tasks. However, the pixel-wise RF classifier ignores contextual relationships between label outputs of individual pixels. RF which only utilizes local appearance features is also susceptible to data suffering from large intensity variations. In this paper, we demonstrate how to overcome the above limitations of classic RF by presenting a fully automatic segmentation pipeline for myocardial segmentation in full-cycle 2-D MCE data. Specifically, a statistical shape model is used to provide shape prior information that guide the RF segmentation in two ways. First, a novel shape model (SM) feature is incorporated into the RF framework to generate a more accurate RF probability map. Second, the shape model is fitted to the RF probability map to refine and constrain the final segmentation to plausible myocardial shapes. We further improve the performance by introducing a bounding box detection algorithm as a preprocessing step in the segmentation pipeline. Our approach on 2-D image is further extended to 2-D+t sequences which ensures temporal consistency in the final sequence segmentations. When evaluated on clinical MCE data sets, our proposed method achieves notable improvement in segmentation accuracy and outperforms other state-of-the-art methods, including the classic RF and its variants, active shape model and image registration.

Research Area(s)

  • contrast echocardiography, convolutional neural network, myocardial segmentation, Random forest, statistical shape model

Citation Format(s)

Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model. / Li, Yuanwei; Ho, Chin Pang; Toulemonde, Matthieu; Chahal, Navtej; Senior, Roxy; Tang, Meng-Xing.

In: IEEE Transactions on Medical Imaging, Vol. 37, No. 5, 05.2018, p. 1081-1091.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review