Detection and Tracking Protein Molecules in Fluorescence Microscopic Video

This paper provides a bioimage informatics system of detecting and tracking protein molecules, called APP-GFPs, in a live-cell video captured by a fluorescent microscope. Since both processes encounter many difficulties such as many targets, less appearance information, and heavy background noise, we will try to design the system as robust as possible. Specifically, for the detection, a machine learning-based method is employed. For tracking, a method based on a global optimization strategy is newly developed. Experimental results showed that the speed and direction distributions of molecular motion by the proposed system were very similar to that by manual inspection.

[1]  Yuan F. Zheng,et al.  Object Tracking With Particle Filtering in Fluorescence Microscopy Images: Application to the Motion of Neurofilaments in Axons , 2012, IEEE Transactions on Medical Imaging.

[2]  A. Sergé,et al.  Dynamic multiple-target tracing to probe spatiotemporal cartography of cell membranes , 2008, Nature Methods.

[3]  Shree K. Nayar,et al.  What Can Be Known about the Radiometric Response from Images? , 2002, ECCV.

[4]  Yu Ohsugi,et al.  The novel cargo Alcadein induces vesicle association of kinesin‐1 motor components and activates axonal transport , 2007, The EMBO journal.

[5]  J. Davies,et al.  Molecular Biology of the Cell , 1983, Bristol Medico-Chirurgical Journal.

[6]  Hanchuan Peng,et al.  Bioimage informatics: a new area of engineering biology , 2008, Bioinform..

[7]  Pascal Fua,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Multiple Object Tracking Using K-shortest Paths Optimization , 2022 .

[8]  K. Jacobson,et al.  Single-particle tracking: applications to membrane dynamics. , 1997, Annual review of biophysics and biomolecular structure.

[9]  D. Grier,et al.  Methods of Digital Video Microscopy for Colloidal Studies , 1996 .

[10]  Harry Shum,et al.  Interactive Offline Tracking for Color Objects , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[11]  Takeo Kanade,et al.  Reliable cell tracking by global data association , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[12]  Andrew W. Fitzgibbon,et al.  Interactive Feature Tracking using K-D Trees and Dynamic Programming , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[13]  Wiro J. Niessen,et al.  Particle Filtering for Multiple Object Tracking in Dynamic Fluorescence Microscopy Images: Application to Microtubule Growth Analysis , 2008, IEEE Transactions on Medical Imaging.