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UNSUPERVISED SHAPE PRIOR MODELING FOR CELL SEGMENTATION IN NEUROENDOCRINE TUMOR
Automated and accurate cell segmentation provides support for many quantitative analyses on digitized neuroendocrine tumor (NET) images. It is a challenging task due to complex variations of cell characteristics. In this paper, we incorporate unsupervised shape priors into an efficient repulsive def...
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| 出版年: | Proc IEEE Int Symp Biomed Imaging |
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| 主要な著者: | , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2015
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5136468/ https://ncbi.nlm.nih.gov/pubmed/27924189 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/ISBI.2015.7164148 |
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