Scratch Assay Image Analysis Automation
| dc.contributor.author | Urrejola-Barrios, Sebastián | |
| dc.contributor.author | Del Campo-Smith, Matías | |
| dc.contributor.author | Durán, Eduardo | |
| dc.contributor.author | Asahi, Takeshi | |
| dc.contributor.author | Opitz, Daniela | |
| dc.contributor.author | Lobos-González, Lorena | |
| dc.date.accessioned | 2023-02-21T17:03:46Z | |
| dc.date.available | 2023-02-21T17:03:46Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | In this brief proof-of-concept paper, we present an algorithm developed in Python to automate the analysis of images obtained in scratch assays. Our algorithm uses random forest, a classic machine learning technique, to train and segment scratch assay images. This enables an average time reduction of 84% on the analysis of the images, together with a procedure with replicable results. | |
| dc.description.version | Versión aceptada | |
| dc.identifier.citation | Urrejola-Barrios, S., del Campo-Smith, M., Duran, E., Asahi, T., Opitz, D., & Lobos-Gonzalez, L. Scratch Assay Image Analysis Automation. Abstract Track 26th MIUA conference University of Cambridge, UK., 106–109 (2022) | |
| dc.identifier.uri | https://repositorio.udd.cl/handle/11447/7028 | |
| dc.language.iso | en | |
| dc.subject | Machine learning | |
| dc.subject | Medical images & software solution | |
| dc.title | Scratch Assay Image Analysis Automation | |
| dc.type | Article | |
| dcterms.source | The 26th Medical Image Understanding and Analysis (MIUA) conference’s abstract track |
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