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| Content Provider | ACM Digital Library |
|---|---|
| Author | Bhanu, Bir Guan, Benjamin X. Talbot, Prue Lin, Sabrina |
| Copyright Year | 2014 |
| Abstract | This paper proposes a bio-driven algorithm that detects cell regions automatically in the human embryonic stem cell (hESC) images obtained using a phase contrast microscope. The algorithm uses both statistical intensity distributions of foreground/hESCs and background/substrate as well as cell property for cell region detection. The intensity distributions of foreground/hESCs and background/substrate are modeled as a mixture of two Gaussians. The cell property is translated into local spatial information. The algorithm is optimized by parameters of the modeled distributions and cell regions evolve with the local cell property. The paper validates the method with various videos acquired using different microscope objectives. In comparison with the state-of-the-art methods, the proposed method is able to detect the entire cell region instead of fragmented cell regions. It also yields high marks on measures such as Jacard similarity, Dice coefficient, sensitivity and specificity. Automated detection by the proposed method has the potential to enable fast quantifiable analysis of hESCs using large data sets which are needed to understand dynamic cell behaviors. |
| Starting Page | 604 |
| Ending Page | 611 |
| Page Count | 8 |
| File Format | |
| ISSN | 15455963 |
| DOI | 10.1109/TCBB.2014.2306836 |
| Volume Number | 11 |
| Issue Number | 3 |
| Journal | IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-05-01 |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Automated detection Bio-driven Bioinformatics Human embryonic stem cell (hESC) |
| Content Type | Text |
| Resource Type | Article |
| Subject | Genetics Biotechnology Applied Mathematics |
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