Prediction Based Collaborative Trackers (PCT): A Robust and Accurate Approach Toward 3D Medical Object Tracking

Abstract

Abstract-Robust and fast 3D tracking of deformable objects, such as heart, is a challenging task because of the relatively low image contrast and speed requirement. Many existing 2D algorithms might not be directly applied on the 3D tracking problem. The 3D tracking performance is limited due to dramatically increased data size, landmarks ambiguity, signal drop-out or complex non-rigid deformation. In this paper we present a robust, fast and accurate 3D tracking algorithm: Prediction Based Collaborative Trackers (PCT). A novel onestep forward prediction is introduced to generate the motion prior using motion manifold learning. Collaborative trackers are introduced to achieve both temporal consistency and failure recovery. Compared with tracking by detection and 3D optical flow, PCT provides the best results. The new tracking algorithm is completely automatic and computationally efficient. It requires less than 1.5 seconds to process a 3D volume which contains millions of voxels. In order to demonstrate the generality of PCT, the tracker is fully tested on three large clinical datasets for three 3D heart tracking problems with two different imaging modalities: endocardium tracking of the left ventricle (67 sequences, 1134 3D volumetric echocardiography data), dense tracking in the myocardial regions between the epicardium and endocardium of the left ventricle (503 sequences, roughly 9000 3D volumetric echocardiography data), and whole heart four chambers tracking (20 sequences, 200 cardiac 3D volumetric CT data). Our datasets are much larger than most studies reported in the literature and we achieve very accurate tracking results compared with human experts' annotations and recent literature.

Automatic Computation of Left Ventricular Volume Changes Over a Cardiac Cycle from Echocardiography Images by Nonlinear Dimensionality Reduction

“…Moreover, these algorithms have been widely used for medical image analysis such as visualization of cardiopulmonary MR images [23] and echocardiography images [24], classification in brain MR images [25], tracking of the LV in 3-D echocardiography images and heart in 3-D CT images [26], segmentation of breast MR images [27], assessment of regional and global wall motion abnormalities in echocardiography images [28], and detection of polyps in CT colonography images [29,30].…”

Learning-Based Detection and Tracking in Medical Imaging: A Probabilistic Approach

“…To obtain these motion characteristics from the pre-annotated databases, we use manifold learning to extract a compact form of the dynamic information [43].…”

“…, n, where u i ∈ R m is the sampling noise and v i ∈ R q denotes the original ith shape m i in the low-dimensional manifold. In the prediction step, the motion prior (state model) p(X t |X t−1 ) is computed using the learned motion modes [43].…”

“…The accuracy is measured by the point-to-mesh error [43] reported in Table 5 for all three methods. The point-to-mesh errors are measured in millimeters.…”

A New Approach for Automatic Identification of the Ventricular Boundary in Cine Magnetic Resonance Images

“…We divide the state of art literatures into three categories: (1) spatial domain methods; [1][2][3][4][5][6] (2) statistical methods; [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] and (3) time domain tracking methods. [26][27][28][29] Many spatial domain intensity methods utilize a global threshold to accurately identify the ventricular cavity from images which have well-defined differences in pixel intensity between the blood pool and the myocardium. However, the left ventricle often has papillary muscles and rough trabeculations which are included in the ventricular cavity for clinical measurements of volumes and ejection fractions.…”