Morphology-Guided Graph Search for Untangling Objects: C. elegans Analysis
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Golland_Morphology-guided graph.pdf
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Author(s)
Raviv, T. Riklin
Ljosa, V.
Conery, Annie L.
Ausubel, Frederick M.
Carpenter, Anne E.
Golland, Polina
Wahlby, C.
Date Issued
2010
Journal
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010
Publisher
Springer-Verlag Berlin Heidelberg
Citation
Raviv, T. Riklin, V. Ljosa, A. L. Conery, F. M. Ausubel, A. E. Carpenter, P. Golland, and C. Wahlby. “Morphology-Guided Graph Search for Untangling Objects: C. Elegans Analysis.” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, Part III. Edited by Tianzi Jiang, Nassir Navab, Josien P. W. Pluim, and Max A Viergever. Springer-Verlag Berlin Heidelberg, (Lecture Notes in Computer Science; volume 6363) (2010): 634–641.
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1007/978-3-642-15711-0_79
