A New Automatic Cancer Colony Forming Units Counting Method
Clonogenic assays are an essential tool to evaluate the survival of cancer cells that have been exposed to a certain dose of radiation. Its result can be used in the generation of strategies for the optimization of radiotherapy treatments. The analysis of this type of data requires that the speciali...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/22529
- Acceso en línea:
- https://doi.org/10.1007/978-3-030-31321-0_40
https://repository.urosario.edu.co/handle/10336/22529
- Palabra clave:
- Cell proliferation
Cells
Cytology
Diseases
Image segmentation
Pattern recognition
Radiotherapy
Cancer
Cell counting
Colony counting
Colony forming units
Radiotherapy treatment
Segmentation algorithms
Sensitivity and specificity
Subjective assessments
Image analysis
Cancer
Cell counting
Cell proliferation
CFU
Colony counting
Image analysis
- Rights
- License
- http://purl.org/coar/access_right/c_abf2
id |
EDOCUR2_565acec4f4a9dafd4350db90efbe52d6 |
---|---|
oai_identifier_str |
oai:repository.urosario.edu.co:10336/22529 |
network_acronym_str |
EDOCUR2 |
network_name_str |
Repositorio EdocUR - U. Rosario |
repository_id_str |
|
spelling |
A New Automatic Cancer Colony Forming Units Counting MethodCell proliferationCellsCytologyDiseasesImage segmentationPattern recognitionRadiotherapyCancerCell countingColony countingColony forming unitsRadiotherapy treatmentSegmentation algorithmsSensitivity and specificitySubjective assessmentsImage analysisCancerCell countingCell proliferationCFUColony countingImage analysisClonogenic assays are an essential tool to evaluate the survival of cancer cells that have been exposed to a certain dose of radiation. Its result can be used in the generation of strategies for the optimization of radiotherapy treatments. The analysis of this type of data requires that the specialist performs the manual counting of colony forming units (CFU), i.e., find every cell that retains the ability to produce a large progeny. This task is time consuming, prone to errors and the results are not reproducible due to specialist subjective assessment. Digital image processing tools can deal with the flaws described above. This article presents a new technique for automatic CFU counting. The proposed technique extracts the regions of interest (ROIs), where a local segmentation algorithm finds and labels the CFUs in order to quantify them. Results show good sensitivity and specificity performance compared to state-of-the-art software used for CFU detection and counting. © 2019, Springer Nature Switzerland AG.Springer20192020-05-25T23:56:49Zinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fapplication/pdfhttps://doi.org/10.1007/978-3-030-31321-0_40https://repository.urosario.edu.co/handle/10336/22529instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURenghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85076116691&doi=10.1007%2f978-3-030-31321-0_40&partnerID=40&md5=472a49850fd7b5b0b1aecf2ab4550dfdhttp://purl.org/coar/access_right/c_abf2Roldán N.Rodriguez L.Hernandez A.Cepeda K.Ondo Méndez, Alejandro OyonoCancino Suárez S.L.Forero M.G.Lopéz J.M.oai:repository.urosario.edu.co:10336/225292022-05-02T07:37:16Z |
dc.title.none.fl_str_mv |
A New Automatic Cancer Colony Forming Units Counting Method |
title |
A New Automatic Cancer Colony Forming Units Counting Method |
spellingShingle |
A New Automatic Cancer Colony Forming Units Counting Method Cell proliferation Cells Cytology Diseases Image segmentation Pattern recognition Radiotherapy Cancer Cell counting Colony counting Colony forming units Radiotherapy treatment Segmentation algorithms Sensitivity and specificity Subjective assessments Image analysis Cancer Cell counting Cell proliferation CFU Colony counting Image analysis |
title_short |
A New Automatic Cancer Colony Forming Units Counting Method |
title_full |
A New Automatic Cancer Colony Forming Units Counting Method |
title_fullStr |
A New Automatic Cancer Colony Forming Units Counting Method |
title_full_unstemmed |
A New Automatic Cancer Colony Forming Units Counting Method |
title_sort |
A New Automatic Cancer Colony Forming Units Counting Method |
dc.subject.none.fl_str_mv |
Cell proliferation Cells Cytology Diseases Image segmentation Pattern recognition Radiotherapy Cancer Cell counting Colony counting Colony forming units Radiotherapy treatment Segmentation algorithms Sensitivity and specificity Subjective assessments Image analysis Cancer Cell counting Cell proliferation CFU Colony counting Image analysis |
topic |
Cell proliferation Cells Cytology Diseases Image segmentation Pattern recognition Radiotherapy Cancer Cell counting Colony counting Colony forming units Radiotherapy treatment Segmentation algorithms Sensitivity and specificity Subjective assessments Image analysis Cancer Cell counting Cell proliferation CFU Colony counting Image analysis |
description |
Clonogenic assays are an essential tool to evaluate the survival of cancer cells that have been exposed to a certain dose of radiation. Its result can be used in the generation of strategies for the optimization of radiotherapy treatments. The analysis of this type of data requires that the specialist performs the manual counting of colony forming units (CFU), i.e., find every cell that retains the ability to produce a large progeny. This task is time consuming, prone to errors and the results are not reproducible due to specialist subjective assessment. Digital image processing tools can deal with the flaws described above. This article presents a new technique for automatic CFU counting. The proposed technique extracts the regions of interest (ROIs), where a local segmentation algorithm finds and labels the CFUs in order to quantify them. Results show good sensitivity and specificity performance compared to state-of-the-art software used for CFU detection and counting. © 2019, Springer Nature Switzerland AG. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 2020-05-25T23:56:49Z |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_c94f |
dc.identifier.none.fl_str_mv |
https://doi.org/10.1007/978-3-030-31321-0_40 https://repository.urosario.edu.co/handle/10336/22529 |
url |
https://doi.org/10.1007/978-3-030-31321-0_40 https://repository.urosario.edu.co/handle/10336/22529 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076116691&doi=10.1007%2f978-3-030-31321-0_40&partnerID=40&md5=472a49850fd7b5b0b1aecf2ab4550dfd |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
instname:Universidad del Rosario reponame:Repositorio Institucional EdocUR |
instname_str |
Universidad del Rosario |
institution |
Universidad del Rosario |
reponame_str |
Repositorio Institucional EdocUR |
collection |
Repositorio Institucional EdocUR |
repository.name.fl_str_mv |
|
repository.mail.fl_str_mv |
|
_version_ |
1803710456899043328 |