Increasing attention has been focused on the estimation of the deformation of the endocardium to aid the diagnosis
of cardiac malfunction. Landmark tracking can provide sparse, anatomically relevant constraints to help establish
correspondences between images being tracked or registered. However, landmarks on the endocardium are often
characterized by ambiguous appearance in cardiac MR images which makes the extraction and tracking of these
In this paper we propose an automatic framework to select and track a sparse set of distinctive landmarks in
the presence of relatively large deformations in order to capture the endocardial motion in cardiac MR sequences.
To achieve this a sparse set of the landmarks is identified using an entropy-based approach. In particular we use
singular value decomposition (SVD) to reduce the search space and localize the landmarks with relatively large
deformation across the cardiac cycle. The tracking of the sparse set of landmarks is performed simultaneously
by optimizing a two-stage Markov Random Field (MRF) model. The tracking result is further used to initialize
registration based dense motion tracking.
We have applied this framework to extract a set of landmarks at the endocardial border of the left ventricle in
MR image sequences from 51 subjects. Although the left ventricle undergoes a number of different deformations,
we show how the radial, longitudinal motion and twisting of the endocardial surface can be captured by the
proposed approach. Our experiments demonstrate that motion tracking using sparse landmarks can outperform
conventional motion tracking by a substantial amount, with improvements in terms of tracking accuracy of 20:8%
and 19:4% respectively.
In this work, we investigate the computation on a shape manifold for atlas generation and application to atlas propagation and segmentation. We formulate the computation of Fréchet mean via the constant velocity fields and Log-Euclidean framework for Nadaraya-Watson kernel regression modeling. In this formulation, we directly compute the Fréchet mean of shapes via fast vectorial operations on the velocity fields. By using image similarity metric to estimate the distance of shapes in the assumed manifold, we can estimate a close shape of an unseen image using Naderaya-Watson kernel regression function. We applied this estimation to generate subject-specific atlases for whole heart segmentation of MRI data. The segmentation results on clinical data demonstrated an improved performance compared to existing methods, thanks to the usage of subject-specific atlases which had more similar shapes to the unseen images.
Optical coherence tomography (OCT) is a light-based, high resolution imaging technique to guide stent deployment
procedure for stenosis. OCT can accurately differentiate the most superficial layers of the vessel wall as
well as stent struts and the vascular tissue surrounding them. In this paper, we automatically detect the struts
of coronary stents present in OCT sequences. We propose a novel method to detect the strut shadow zone and
accurately segment and reconstruct the strut in 3D. The estimation of the position of the strut shadow zone
is the key requirement which enables the strut segmentation. After identification of the shadow zone we use
probability map to estimate stent strut positions. This method can be applied to cross-sectional OCT images
to detect the struts. Validation is performed using simulated data as well as in four in-vivo OCT sequences and
the accuracy of strut detection is over 90%. The comparison against manual expert segmentation demonstrates
that the proposed strut identification is robust and accurate.