Segmenting and tracking the left ventricle by learning the dynamics in cardiac images

  • Authors:

  • W. Sun;M. Çetin;R. Chan;V. Reddy;G. Holmvang;V. Chandar;A. Willsky

  • Affiliations:

  • Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA;Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA;Cardiovascular MR-CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA;Cardiovascular MR-CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA;Cardiovascular MR-CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA;Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA;Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA

  • Venue:
  • IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
  • Year:
  • 2005

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Abstract

Having accurate left ventricle (LV) segmentations across a cardiac cycle provides useful quantitative (e.g. ejection fraction) and qualitative information for diagnosis of certain heart conditions. Existing LV segmentation techniques are founded mostly upon algorithms for segmenting static images. In order to exploit the dynamic structure of the heart in a principled manner, we approach the problem of LV segmentation as a recursive estimation problem. In our framework, LV boundaries constitute the dynamic system state to be estimated, and a sequence of observed cardiac images constitute the data. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past segmentations. This requires a dynamical system model of the LV, which we propose to learn from training data through an information-theoretic approach. To incorporate the learned dynamic model into our segmentation framework and obtain predictions, we use ideas from particle filtering. Our framework uses a curve evolution method to combine such predictions with the observed images to estimate the LV boundaries at each time. We demonstrate the effectiveness of the proposed approach on a large set of cardiac images. We observe that our approach provides more accurate segmentations than those from static image segmentation techniques, especially when the observed data are of limited quality.