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- W1567117440 abstract "Stem cells are a potential source of cells for use in the regenerative medicine. Automation of monitoring and analysis is crucial for reliable and fast optimization of culturing methods. The goal of the first step in this investigation is to find a method of automatic cell counting on microscopic static images. In our method, an image is divided into two types of regions: the regions covered by cells and the background regions. Next the cell regions are classified into three categories: converged cells,the flatten cells and the transitional cells regions. For each type of region, the adjusted procedure estimates a quantity of cells. The quantity of cells in image has been obtained for randomly chosen images from certain sequences captured from cells which growth has been monitored using laser scanner confocal microscopy. The results of the automatic cell counting are compared with results obtained by an operator and the difference has been admissible. When a lot of frames and cells are counted, the accuracy of the proposed method has been similar to the accuracy of an expert.KeywordsGray LevelNeural Stem CellTransitional CellAutomatic Cell CountingDetectable TextureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
- W1567117440 created "2016-06-24" @default.
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- W1567117440 date "2007-01-01" @default.
- W1567117440 modified "2023-09-26" @default.
- W1567117440 title "Automatic Counting of Neural Stem Cells Growing in Cultures" @default.
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- W1567117440 doi "https://doi.org/10.1007/978-3-540-75175-5_76" @default.
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