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| Content Provider | PubMed Central |
|---|---|
| Author | Higaki, Takumi Kutsuna, Natsumaro Akita, Kae Sato, Mayuko Sawaki, Fumie Kobayashi, Megumi Nagata, Noriko Toyooka, Kiminori Hasezawa, Seiichiro |
| Copyright Year | 2015 |
| Abstract | Recent advances in the acquisition of large-scale datasets of transmission electron microscope images have allowed researchers to determine the number and the distribution of subcellular ultrastructures at both the cellular level and the tissue level. For this purpose, it would be very useful to have a computer-assisted system to detect the structures of interest, such as organelles. Using our original image recognition framework CARTA (Clustering-Aided Rapid Training Agent), combined with procedures to highlight and enlarge regions of interest on the image, we have developed a successful method for the semi-automatic detection of plant organelles including mitochondria, amyloplasts, chloroplasts, etioplasts, and Golgi stacks in transmission electron microscope images. Our proposed semi-automatic detection system will be helpful for labelling organelles in the interpretation and/or quantitative analysis of large-scale electron microscope imaging data. |
| Related Links | http://dx.doi.org/10.1038/srep07794 |
| Starting Page | 7794 |
| File Format | |
| ISSN | 20452322 |
| e-ISSN | 20452322 |
| Journal | Scientific Reports |
| Volume Number | 5 |
| Language | English |
| Publisher | Nature Publishing Group |
| Publisher Date | 2015-01-01 |
| Access Restriction | Open |
| Rights Holder | Nature Publishing Group |
| Subject Keyword | Science and technology Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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