Validation-data Generation for Brightfield Microscopy Cell Tracking using Fluorescence Samples
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 wi...
| Autores Principales: | Quinde-Cobos, Patricia, Quirós, Steve, Siles-Canales, Francisco |
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| Formato: | Artículo |
| Idioma: | Inglés |
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Editorial Tecnológica de Costa Rica (entidad editora)
2020
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| Acceso en línea: |
https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/5083 https://hdl.handle.net/2238/12076 |
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RepoTEC12076 |
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RepoTEC120762020-09-25T23:12:53Z Validation-data Generation for Brightfield Microscopy Cell Tracking using Fluorescence Samples Generación de Datos de Validación para Rastreo Celular en Microscopía de Campo Claro usando Muestras Fluorescentes Quinde-Cobos, Patricia Quirós, Steve Siles-Canales, Francisco Cancer brightfield microscopy fluorescence microscopy pattern recognition Cáncer microscopía de campo claro microscopía de fluorescencia reconocimiento de patrones 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. Este trabajo usa imágenes de fluorescencia de células cancerígenas como para validación de resultados obtenidos por segmentación de imágenes de campo claro de células cancerígenas, ya que actualmente la validación consiste en la anotación manual de las imágenes originales. Se usó reconocimiento de patrones y se inició con preprocesamiento de muestras fluorescentes para asegurar la detección de células, considerando área y valores de intensidad. Al segmentar las imágenes de fluorescencia el núcleo de cada célula es detectado, con su forma original. Al calcular la densidad de cada imagen, estas pueden ser agrupadas de acuerdo a su valor de densidad y usadas para la detección de células en muestras de campo claro. 2020-03-27 2020-09-25T23:12:53Z 2020-09-25T23:12:53Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/5083 10.18845/tm.v33i5.5083 https://hdl.handle.net/2238/12076 eng https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/5083/4804 application/pdf Editorial Tecnológica de Costa Rica (entidad editora) Tecnología en marcha Journal; 2020: Vol. 33 especial. Contribuciones a la Conferencia 6th Latin America High Performance Computing Conference (CARLA); Pág. 91-95 Revista Tecnología en Marcha; 2020: Vol. 33 especial. Contribuciones a la Conferencia 6th Latin America High Performance Computing Conference (CARLA); Pág. 91-95 2215-3241 0379-3982 |
| institution |
Tecnológico de Costa Rica |
| collection |
Repositorio TEC |
| language |
Inglés |
| topic |
Cancer brightfield microscopy fluorescence microscopy pattern recognition Cáncer microscopía de campo claro microscopía de fluorescencia reconocimiento de patrones |
| spellingShingle |
Cancer brightfield microscopy fluorescence microscopy pattern recognition Cáncer microscopía de campo claro microscopía de fluorescencia reconocimiento de patrones Quinde-Cobos, Patricia Quirós, Steve Siles-Canales, Francisco Validation-data Generation for Brightfield Microscopy Cell Tracking using Fluorescence Samples |
| description |
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. |
| format |
Artículo |
| author |
Quinde-Cobos, Patricia Quirós, Steve Siles-Canales, Francisco |
| author_sort |
Quinde-Cobos, Patricia |
| title |
Validation-data Generation for Brightfield Microscopy Cell Tracking using Fluorescence Samples |
| title_short |
Validation-data Generation for Brightfield Microscopy Cell Tracking using Fluorescence Samples |
| title_full |
Validation-data Generation for Brightfield Microscopy Cell Tracking using Fluorescence Samples |
| title_fullStr |
Validation-data Generation for Brightfield Microscopy Cell Tracking using Fluorescence Samples |
| title_full_unstemmed |
Validation-data Generation for Brightfield Microscopy Cell Tracking using Fluorescence Samples |
| title_sort |
validation-data generation for brightfield microscopy cell tracking using fluorescence samples |
| publisher |
Editorial Tecnológica de Costa Rica (entidad editora) |
| publishDate |
2020 |
| url |
https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/5083 https://hdl.handle.net/2238/12076 |
| _version_ |
1796140571783331840 |
| score |
12.041432 |