Performance of optical flow techniques for motion analysis of fluorescent point signals in confocal microscopy

Abstract

Artículo de publicación ISIOptical flow approaches calculate vector fields which determine the apparent velocities of objects in timevarying image sequences. They have been analyzed extensively in computer science using both natural and synthetic video sequences. In life sciences, there is an increasing need to extract kinetic information from temporal image sequences which reveals the interplay between form and function of microscopic biological structures. In this work, we test different variational optical flow techniques to quantify the displacements of biological objects in 2D fluorescent image sequences. The accuracy of the vector fields is tested for defined displacements of fluorescent point sources in synthetic image series which mimic protein traffic in neuronal dendrites, and for GABABR1 receptor subunits in dendrites of hippocampal neurons. Our results reveal that optical flow fields predict the movement of fluorescent point sources within an error of 3% for a maximum displacement of 160 nm. Displacement of agglomerated GABABR1 receptor subunits can be predicted correctly for maximum displacements of 640 nm. Based on these results, we introduce a criteria to derive the optimum parameter combinations for the calculation of the optical flow fields in experimental images. From these results, temporal sampling frequencies for image acquisition can be derived to guarantee correct motion estimation for biological objects.J. Delpiano and J. Jara are funded by a PhD scholarship from CONICYT (Chile). O. A. Ramírez is funded by FONDECYT (3110157). Research in SCIAN-Lab (S. Härtel) is funded by FONDECYT (1090246) and FONDEF (D07I1019). SCIAN-Lab is member of the German-Chilean Center of Excellence Initiative for Medical Informatics (DAAD), BNI (ICM P09-015-F), and the Advanced Imaging & Bioinformatics Initiative AI·BI (http://www.aibi.cl)

An Implementation of Combined Local-Global Optical Flow

“…Figures 2-5 and the variability of the error metrics illustrate this issue, as well as reported results from Delpiano et al [5], Hubený et al [9], Meinhardt-Llopis et al [11] and Sun et al [13].…”

“…This also occurs for other OF approaches and numerical schemes, as shown by Delpiano et al [5] and Hubený et al [9] for fluorescent moving objects in microscopy images. See Sun et al [13] for a general overview.…”

“…The signal was convolved with a theoretical point spread function (PSF, b-c) of a confocal microscope (a PSF acts as a smoothing function that blurs signals). See details of simulation and tests for optimal parameters in the work of Delpiano et al [5]. α=20, ρ=0 α=200, ρ=0 α=2000, ρ=0 α=20, ρ=5 α=200, ρ=5 α=2000, ρ=5 α=20, ρ=10 α=200, ρ=10 α=2000, ρ=10 α=20, ρ=0 α=200, ρ=0 α=2000, ρ=0 α=20, ρ=5 α=200, ρ=5 α=2000, ρ=5 α=20, ρ=10 α=200, ρ=10 α=2000, ρ=10 True flow α=200, ρ=0 α=2000, ρ=0 α=20, ρ=5 α=200, ρ=5 α=2000, ρ=5 α=20, ρ=10 α=200, ρ=10 α=2000, ρ=10 True flow α=20, ρ=0 α=200, ρ=0 α=2000, ρ=0 α=20, ρ=5 α=200, ρ=5 α=2000, ρ=5 α=20, ρ=10 α=200, ρ=10 α=2000, ρ=10 True flow …”

A Guided Tour of Selected Image Processing and Analysis Methods for Fluorescence and Electron Microscopy

“…However tracking methods are not always adapted for motion analysis, especially when the density and the lack of prominent features prevent the individual extraction of objects of interest undergoing complex motion. Accordingly, estimating motion fields can be then more appropriate to capture complex dynamics observed in biological sequences [247], [248]. The usual approach for optical flow estimates the dense motion field by minimizing a global energy functional composed of two terms: (7) where is the dense motion field, is a data term penalizing deviations from a data conservation assumption over time, is a regularization term enforcing smoothness of the flow field and serves as regularization parameter to balance and contributions.…”

“…A high value of allows to retrieve only dominant motions of large structures by smoothing the flow field, while a small value of tolerates repeated close spatial variations corresponding to small objects. Applications of global regularized method in biological imaging have recently been investigated in [249], [250], [248], [251]- [254]. Because of possible intensity changes (e.g., photobleaching), the data term needs to be adapted [255].…”