Image-based characterization of thrombus formation in time-lapse DIC microscopy
- Authors
- Brieu, N., Navab, N., Serbanovic-Canic, J., Ouwehand, W.H., Stemple, D.L., Cvejic, A., and Groher, M.
- ID
- ZDB-PUB-120410-5
- Date
- 2012
- Source
- Medical image analysis 16(4): 915-931 (Journal)
- Registered Authors
- Stemple, Derek L.
- Keywords
- time-lapse microscopy, DIC microscopy, motion-segmentation, dynamic texture, tracking
- MeSH Terms
-
- Algorithms*
- Animals
- Image Enhancement/methods
- Image Interpretation, Computer-Assisted/methods*
- Microscopy, Phase-Contrast/methods*
- Pattern Recognition, Automated/methods*
- Reproducibility of Results
- Sensitivity and Specificity
- Subtraction Technique*
- Thrombosis/pathology*
- Time-Lapse Imaging/methods*
- Zebrafish
- PubMed
- 22482997 Full text @ Med. Image Anal.
The characterization of thrombus formation in time-lapse DIC microscopy is of increased interest for identifying genes which account for atherothrombosis and coronary artery diseases (CADs). In particular, we are interested in large-scale studies on zebrafish, which result in large amount of data, and require automatic processing. In this work, we present an image-based solution for the automatized extraction of parameters quantifying the temporal development of thrombotic plugs. Our system is based on the joint segmentation of thrombotic and aortic regions over time. This task is made difficult by the low contrast and the high dynamic conditions observed in vivo DIC microscopic scenes. Our key idea is to perform this segmentation by distinguishing the different motion patterns in image time series rather than by solving standard image segmentation tasks in each image frame. Thus, we are able to compensate for the poor imaging conditions. We model motion patterns by energies based on the idea of dynamic textures, and regularize the model by two prior energies on the shape of the aortic region and on the topological relationship between the thrombus and the aorta. We demonstrate the performance of our segmentation algorithm by qualitative and quantitative experiments on synthetic examples as well as on real in vivo microscopic sequences.