Tracking clathrin coated pits with a multiple hypothesis based method

Authors:
Liang Liang;Hongying Shen;Pietro De Camilli;James S. Duncan
Affiliations:
Yale University, New Haven, CT;Yale University, New Haven, CT;Yale University, New Haven, CT;Yale University, New Haven, CT
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Year:
2010

Citing 6
Cited 1

Resolving Motion Correspondence for Densely Moving Points

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Softassign Procrustes Matching Algorithm

IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Reliable Tracking of Large Scale Dense Antiparallel Particle Motion for Fluorescence Live Cell Imaging

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Object tracking: A survey

ACM Computing Surveys (CSUR)
Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol

IEEE Transactions on Pattern Analysis and Machine Intelligence
Some assignment problems arising from multiple target tracking

Mathematical and Computer Modelling: An International Journal

An expectation maximization based method for subcellular particle tracking using multi-angle TIRF microscopy

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Cellular processes are crucial for cells to survive and function properly. To study their underlying mechanisms quantitatively with fluorescent live cell microscopy, it is necessary to track a large number of particles involved in these processes. In this paper, we present a method to automatically track particles, called clathrin coated pits (CCPs), which are formed in clathrin mediated endocytosis (CME). The tracking method is developed based on a MAP framework, and it consists of particle detection and trajectory estimation. To detect particles in 2D images and take account of Poisson noise, a Gaussian mixture model is fitted to image data, for which initial parameters are provided by a combination of image filtering and histogram based thresholding methods. A multiple hypothesis based algorithm is developed to estimate the trajectories based on detection data. To use the current knowledge about CCPs, their properties of motion and intensity are considered in our models. The tracking method is evaluated on synthetic data and real data, and experimental results show that it has high accuracy and is in good agreement with manual tracking.