Validation-data Generation for Brightfield Microscopy Cell Tracking using Fluorescence Samples
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| Format: | Artículo científico |
| Sprache: | es |
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Instituto Tecnológico de Costa Rica
2020
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| _version_ | 1876476223177097216 |
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| author | Patricia Quinde-Cobos |
| author_facet | Patricia Quinde-Cobos |
| contents | Validation-data Generation for Brightfield Microscopy Cell Tracking using Fluorescence Samples Patricia Quinde-Cobos Steve Quirós Francisco Siles-Canales Ingeniería cancer pattern recognition Brightfield microscopy fluorescence microscopy This work focuses on the use of fluorescent cancer cell images as data to validate the results obtained in segmenting brightfield cancer cell images, as the latter’s current validation consists of manual annotation of cells in the original images. The procedure uses pattern recognition and starts with preprocessing the fluorescent samples to ensure cell detection, focused on area and intensity value. As the fluorescent images are segmented, each cell’s nucleus is detected and counted, with a high success rate as each nucleus’s contour was detected with its original shape. As each image’s density is calculated, they can be clustered according to their density value and used for cell detection in brightfield samples. 2020 artículo científico 2215-3241 https://www.redalyc.org/articulo.oa?id=699878645016 es http://www.redalyc.org/revista.oa?id=6998 Tecnología en marcha application/pdf Instituto Tecnológico de Costa Rica Tecnología en marcha (Costa Rica) Num.CARLA Vol.33 |
| format | Artículo científico |
| id | redalyc_699878645016 |
| institution | Redalyc |
| language | es |
| publishDate | 2020 |
| publisher | Instituto Tecnológico de Costa Rica |
| spellingShingle | Validation-data Generation for Brightfield Microscopy Cell Tracking using Fluorescence Samples Patricia Quinde-Cobos Ingeniería cancer pattern recognition Brightfield microscopy fluorescence microscopy Validation-data Generation for Brightfield Microscopy Cell Tracking using Fluorescence Samples Patricia Quinde-Cobos Steve Quirós Francisco Siles-Canales Ingeniería cancer pattern recognition Brightfield microscopy fluorescence microscopy This work focuses on the use of fluorescent cancer cell images as data to validate the results obtained in segmenting brightfield cancer cell images, as the latter’s current validation consists of manual annotation of cells in the original images. The procedure uses pattern recognition and starts with preprocessing the fluorescent samples to ensure cell detection, focused on area and intensity value. As the fluorescent images are segmented, each cell’s nucleus is detected and counted, with a high success rate as each nucleus’s contour was detected with its original shape. As each image’s density is calculated, they can be clustered according to their density value and used for cell detection in brightfield samples. 2020 artículo científico 2215-3241 https://www.redalyc.org/articulo.oa?id=699878645016 es http://www.redalyc.org/revista.oa?id=6998 Tecnología en marcha application/pdf Instituto Tecnológico de Costa Rica Tecnología en marcha (Costa Rica) Num.CARLA Vol.33 |
| title | Validation-data Generation for Brightfield Microscopy Cell Tracking using Fluorescence Samples |
| topic | Ingeniería cancer pattern recognition Brightfield microscopy fluorescence microscopy |
| url | https://www.redalyc.org/articulo.oa?id=699878645016 |