Image-based Quantification of Angiogenesis Assays
Spheroid Sprouting Assays
Endothelial cells seeded onto a gel matrix condense to a spheroid. Based on bright-field microscopy, the growth of spheroid extensions, so called sprouts, is assessed by measuring the following quantities:
- Area occupied by spheroid
- Area occupied by sprouts
- Length and number of sprouts
Automated Quantification Pipeline
Our goal is to quantify the growth of sprouts depicted in a bright-field microscopy image. We segment the input image using the mPb contour detector (Arbelaez et al., 2011) and then perform a skeletonization (Zhang and Suen, 1984), from which the sprouts are derived.
The spheroid area is detected using a Markov Random Field texture classifier (Varma and Zisserman, 2003).
In a preliminary evaluation, we compared the classification output of the newly developed image processing pipeline to a manual classification. A large-scale comparative study is subject to future work.
Our future goal is to taylor the presented image pipeline to other assay types and optical microscopy modalities, for example:
- Spheroid sprouting assays based on fluorescence microscopy
- Tube assays (determine length of tubes and number of branching points)
Arbelaez, P., Maire, M., Fowlkes, C., & Malik, J. (2011). Contour detection and hierarchical image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(5), 898-916.
Varma, M., & Zisserman, A. (2003, June). Texture classification: Are filter banks necessary?. In Computer vision and pattern recognition, 2003. Proceedings. 2003 IEEE computer society conference on (Vol. 2, pp. II-691). IEEE.
Zhang, T. Y., & Suen, C. Y. (1984). A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 27(3), 236-239.