A computer vision approach to rare cell in vivo fluorescence flow cytometry. Issue 12 (December 2013)

• Record Type: Journal Article
• Authors: Markovic, Stacey; Li, Binlong; Pera, Vivian; Sznaier, Mario; Camps, Octavia; Niedre, Mark
• Is Part Of: Cytometry. Volume 83:Issue 12(2013:Dec.)
• Page Start: 1113
• Page End: 1123
• Publication Date: 2013-12
• Subjects: Flow cytometry -- Periodicals; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnostic imaging -- Periodicals; 571.605
• DOI: 10.1002/cyto.a.22397
• Languages: English
• ISSNs: 1552-4922

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>Noninvasive enumeration of rare circulating cell populations in small animals is of great importance in many areas of biomedical research. In this work, we describe a macroscopic fluorescence imaging system and automated computer vision algorithm that allows in vivo detection, enumeration and tracking of circulating fluorescently‐labeled cells from multiple large blood vessels in the ear of a mouse. This imaging system uses a 660 nm laser and a high sensitivity electron‐multiplied charge coupled device camera (EMCCD) to acquire fluorescence image sequences from relatively large (∼5 × 5 mm<sup>2</sup>) imaging areas. The primary technical challenge was developing an automated method for identifying and tracking rare cell events in image sequences with substantial autofluorescence and noise content. To achieve this, we developed a two‐step image analysis algorithm that first identified cell candidates in individual frames, and then merged cell candidates into tracks by dynamic analysis of image sequences. The second step was critical since it allowed rejection of >97% of false positive cell counts. Overall, our computer vision IVFC (CV‐IVFC) approach allows single‐cell detection sensitivity at estimated concentrations of 20 cells/mL of peripheral blood. In addition to simple enumeration, the technique recovers the cell's trajectory, which in the future could be used to automatically identify, for example, in vivo homing and docking events. © 2013 International Society for Advancement of Cytometry</p> </abstract>