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

Content Provider	IEEE Xplore Digital Library
Author	Lin Yang Georgescu, B. Yefeng Zheng Yang Wang Meer, P. Comaniciu, D.
Copyright Year	1982
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 nonrigid deformation. In this paper, we present a robust, fast, and accurate 3D tracking algorithm: prediction based collaborative trackers (PCT). A novel one-step 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 s 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.
Sponsorship	IEEE Engineering in Medicine and Biology Society IEEE Nuclear and Plasma Sciences Society IEEE Signal Processing Society IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society
Page Count	12
File Size	2104047
Starting Page	1921
Ending Page	1932
File Format	PDF
ISSN	02780062
Volume Number	30
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cardiology Three dimensional displays Computed tomography Motion learning Heart Ultrasonic imaging Tracking ultrasound Computed tomography (CT) heart left ventricle motion learning tracking
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications Radiological and Ultrasound Technology Software

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

Prediction based collaborative trackers (pct): a robust and accurate approach toward 3d medical object tracking.

A fast and accurate tracking algorithm of left ventricles in 3D echocardiography

3D ultrasound tracking of the left ventricle using one-step forward prediction and data fusion of collaborative trackers

3D ultrasound tracking of the left ventricle using one-step forward prediction and data fusion of collaborative trackers (2008)

D.: 3d ultrasound tracking of the left ventricle using one-step forward prediction and data fusion of collaborative trackers (2008)

D.: 3d ultrasound tracking of the left ventricle using one-step forward prediction and data fusion of collaborative trackers (2008)

3D ultrasound tracking of the left ventricles using one-step forward prediction and data fusion of collaborative trackers (2008)

A fast and accurate tracking algorithm of left ventricles in 3D echocardiography

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

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