Fast and robust optical flow for time-lapse microscopy using super-voxels
- Authors
- Amat, F., Myers, E.W., and Keller, P.J.
- ID
- ZDB-PUB-121220-35
- Date
- 2013
- Source
- Bioinformatics (Oxford, England) 29(3): 373-380 (Journal)
- Registered Authors
- Amat, Fernando, Keller, Philipp
- Keywords
- none
- MeSH Terms
-
- Animals
- Drosophila/cytology
- Drosophila/embryology
- Imaging, Three-Dimensional/methods*
- Microscopy/methods*
- Time-Lapse Imaging/methods*
- Zebrafish/embryology
- PubMed
- 23242263 Full text @ Bioinformatics
Motivation: Optical flow is a key method used for quantitative motion estimation of biological structures in light microscopy. It has also been used as a key module in segmentation and tracking systems and is considered a mature technology in the field of computer vision. However, most of the research focused on two-dimensional natural images, which are small in size and rich in edges and texture information. In contrast, three-dimensional (3D) time-lapse recordings of biological specimens comprise up to several terabytes of image data and often exhibit complex object dynamics as well as blurring due to the pointspread-function of the microscope. Thus, new approaches to optical flow are required to improve performance for such data
Results: We solve optical flow in large 3D+time microscopy datasets by defining a Markov Random Field (MRF) over super-voxels in the foreground and applying motion smoothness constraints between super-voxels instead of voxel-wise. This model is tailored to the specific characteristics of light microscopy datasets: super-voxels help registration in textureless areas, the MRF over super-voxels efficiently propagates motion information between neighboring cells, and the background subtraction and super-voxels reduce the dimensionality of the problem by an order of magnitude. We validate our approach on large 3D+time datasets of Drosophila and zebrafish development by analyzing cell motion patterns. We show that our approach is on average 10× faster than commonly used optical flow implementations in the Insight Tool-Kit (ITK) and reduces the average flow endpoint error by 50% in regions with complex dynamic processes, such as cell divisions.