Copyright © 2014 by Intelligent Systems Laboratory, Computer Science Department, Technion - Israel Institute of Technology, Haifa 3200003, Israel. All rights reserved

Celltrack - An Automated Method For Analysis Of Circulating Particles Flow

Celltrack - An Automated Method For Analysis Of Circulating Particles FlowBy: Eran Eden, Dan Waisman, Michael Rudzsky, Haim Bitterman, Vera Brod and Ehud Rivlin

The behavior of white and red blood cells, platelets, and circulating injected particles is one of the most studied areas of physiology. Most methods used to analyze the circulatory patterns of cells are time consuming. We describe a system named CellTrack, designed for fully automated tracking of circulating cells and micro-particles and retrieval of their behavioral characteristics. The task of automated blood cell tracking in vessels from in vivo video is particularly challenging because of the blood cells’ nonrigid shapes, the instability inherent in in vivo videos, the abundance of moving objects and their frequent superposition.


To tackle this, the CellTrack system operates on two levels.

A global processing module extracts vessel borders and center lines based on color and temporal patterns. This enables the computation of the approximate direction of the blood flow in each vessel.

A local processing module extracts the locations and velocities of circulating cells. This is performed by artificial neural network classifiers that are designed to detect specific types of blood cells and micro-particles.

The motion correspondence problem is then resolved by a novel algorithm that incorporates both the local and the global information. The system has been tested on a series of in vivo color video recordings of rat mesentery. Our results show that the synergy between the global and local information enables CellTrack to overcome many of the difficulties inherent in tracking methods that rely solely on local information.

A comparison was made between manual measurements and the automatically extracted measurements of leukocytes and fluorescent microspheres circulatory velocities. This comparison revealed an accuracy of 97%. CellTrack also enabled a much larger volume of sampling in a fraction of time compared to the manual measurements. All these results suggest that our method can in fact constitute a reliable replacement for manual extraction of blood flow characteristics from in vivo videos.

Presentation Download: (PPT)


An Automated Method for Analysis of Flow Characteristics of Circulating Particles From In vivo Video Microscopy
Eden, E.; Waisman, D.; Rudzsky, M.; Bitterman, H.; Brod, V.; Rivlin, E.
IEEE Transactions of Medical Imaging, vol. 24 Issue: 8, pp. 1011- 1024, Aug. 2005.


Examples of Type I and Type II in-vivo videos used for the development and testing of the CellTrack system: