Morphology-Guided Graph Search for Untangling Objects: C. elegans Analysis

Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 12/2010; 13(Pt 3):634-41. DOI: 10.1007/978-3-642-15711-0_79
Source: PubMed


We present a novel approach for extracting cluttered objects based on their morphological properties. Specifically, we address the problem of untangling Caenorhabditis elegans clusters in high-throughput screening experiments. We represent the skeleton of each worm cluster by a sparse directed graph whose vertices and edges correspond to worm segments and their adjacencies, respectively. We then search for paths in the graph that are most likely to represent worms while minimizing overlap. The worm likelihood measure is defined on a low-dimensional feature space that captures different worm poses, obtained from a training set of isolated worms. We test the algorithm on 236 microscopy images, each containing 15 C. elegans worms, and demonstrate successful cluster untangling and high worm detection accuracy.

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    • "It is also relatively easy to incorporate continuous object-level regularization into level sets, such as shape priors. Another type of energy-based model is based on graph search [16,17], graph cuts [18,19] or normalized cuts [20]. Such methods attempt to derive the segmentation with global constraints, using well-defined graphical structures to represent the spatial relationships between regions. "
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