Aggregation of patch-based estimations for illumination-invariant optical flow in live cell imaging

Live cell image sequences provide a large variety of challenging situations for motion estimation. We present a novel optical flow estimation method based on a two-stage aggregation framework and designed to handle this diversity of issues. First, semi-local candidates are estimated with a combination of patch correspondences and illumination-invariant affine motion estimations. Then, one candidate is selected at each pixel in a graph-cut based global aggregation stage. This approach allows us to overcome usual limitations of existing methods such as loss of small structures with large displacements, dependency on illumination changes and oversmoothing of the discontinuities. The method jointly estimates the motion and illumination field. We compare our approach to state-of-the-art methods and demonstrate its ability to outperform them in challenging cases frequently arising in live cell imaging.

1. INTRODUCTION

Estimating motion in live cell imaging is a fundamental task to analyse dynamic properties of biological phenomena [1], [2]. The difficulties arise from the variety of situations encountered in microscopy images. Indeed, we have to deal with different imaging modalities (e.g. fluorescence microscopy, contrast-phase imaging) in order to observe interacting structures with different sizes and shapes such as cells, vesicles, microtubules, with different types of motion and deformation. A lot of works have addressed specific biological issues [3], [4], [5], [6], but none general framework is currently available. In this paper, we propose a novel optical flow estimation method providing accurate motion fields in various challenging conditions occuring in live cell image sequences.