Morphology-Guided Graph Search for Untangling Objects: C. elegans Analysis
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en_US |
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Springer-Verlag Berlin Heidelberg
2014
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| Online Access: | http://hdl.handle.net/1721.1/86959 https://orcid.org/0000-0003-2516-731X |
| _version_ | 1826188303979773952 |
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| author | Raviv, T. Riklin Ljosa, V. Conery, Annie L. Ausubel, Frederick M. Carpenter, Anne E. Golland, Polina Wahlby, C. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Raviv, T. Riklin Ljosa, V. Conery, Annie L. Ausubel, Frederick M. Carpenter, Anne E. Golland, Polina Wahlby, C. |
| author_sort | Raviv, T. Riklin |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T07:57:19Z |
| format | Article |
| id | mit-1721.1/86959 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T07:57:19Z |
| publishDate | 2014 |
| publisher | Springer-Verlag Berlin Heidelberg |
| record_format | dspace |
| spelling | mit-1721.1/869592022-09-23T09:52:05Z Morphology-Guided Graph Search for Untangling Objects: C. elegans Analysis Raviv, T. Riklin Ljosa, V. Conery, Annie L. Ausubel, Frederick M. Carpenter, Anne E. Golland, Polina Wahlby, C. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Golland, Polina Raviv, T. Riklin 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. National Institutes of Health (U.S.) (NIH grant R01-AI072508) National Institutes of Health (U.S.) (P01-AI083214) National Institutes of Health (U.S.) (R01-AI085581) National Institutes of Health (U.S.) (NAMIC U54-EB00514) National Science Foundation (U.S.) (NSF CAREER Award 0642971) 2014-05-14T20:44:24Z 2014-05-14T20:44:24Z 2010 Article http://purl.org/eprint/type/ConferencePaper 978-3-642-15710-3 978-3-642-15711-0 0302-9743 1611-3349 http://hdl.handle.net/1721.1/86959 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. https://orcid.org/0000-0003-2516-731X en_US http://dx.doi.org/10.1007/978-3-642-15711-0_79 Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer-Verlag Berlin Heidelberg PMC |
| spellingShingle | Raviv, T. Riklin Ljosa, V. Conery, Annie L. Ausubel, Frederick M. Carpenter, Anne E. Golland, Polina Wahlby, C. Morphology-Guided Graph Search for Untangling Objects: C. elegans Analysis |
| title | Morphology-Guided Graph Search for Untangling Objects: C. elegans Analysis |
| title_full | Morphology-Guided Graph Search for Untangling Objects: C. elegans Analysis |
| title_fullStr | Morphology-Guided Graph Search for Untangling Objects: C. elegans Analysis |
| title_full_unstemmed | Morphology-Guided Graph Search for Untangling Objects: C. elegans Analysis |
| title_short | Morphology-Guided Graph Search for Untangling Objects: C. elegans Analysis |
| title_sort | morphology guided graph search for untangling objects c elegans analysis |
| url | http://hdl.handle.net/1721.1/86959 https://orcid.org/0000-0003-2516-731X |
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