Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107545
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dc.contributor.authorCarneiro, G.en
dc.contributor.authorPeng, T.en
dc.contributor.authorBayer, C.en
dc.contributor.authorNavab, N.en
dc.date.issued2015en
dc.identifier.citationProceedings of the 2015 IEEE International Conference on Image Processing, 2015 / vol.2015-December, pp.2429-2433en
dc.identifier.isbn9781479983391en
dc.identifier.issn1522-4880en
dc.identifier.urihttp://hdl.handle.net/2440/107545-
dc.description.abstractThe efficacy of cancer treatments (e.g., radiotherapy, chemotherapy, etc.) has been observed to critically depend on the proportion of hypoxic regions (i.e., a region deprived of adequate oxygen supply) in tumor tissue, so it is important to estimate this proportion from histological samples. Medical imaging data can be used to classify tumor tissue regions into necrotic or vital and then the vital tissue into normoxia (i.e., a region receiving a normal level of oxygen), chronic or acute hypoxia. Currently, this classification is a lengthy manual process performed using (immuno-)fluorescence (IF) and hematoxylin and eosin (HE) stained images of a histological specimen, which requires an expertise that is not widespread in clinical practice. In this paper, we propose a fully automated way to detect and classify tumor tissue regions into necrosis, normoxia, chronic hypoxia and acute hypoxia using IF and HE images from the same histological specimen. Instead of relying on any single classification methodology, we propose a principled combination of the following current state-of-the-art classifiers in the field: Adaboost, support vector machine, random forest and convolutional neural networks. Results show that on average we can successfully detect and classify more than 87% of the tumor tissue regions correctly. This automated system for estimating the proportion of chronic and acute hypoxia could provide clinicians with valuable information on assessing the efficacy of cancer treatments.en
dc.description.statementofresponsibilityGustavo Carneiro, Tingying Peng, Christine Bayer, Nassir Navaben
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesIEEE International Conference on Image Processing ICIPen
dc.rights© 2015 IEEEen
dc.subjectCytological microscopic images, multimodal classification, classifier combinationen
dc.titleAutomatic detection of necrosis, normoxia and hypoxia in tumors from multimodal cytological imagesen
dc.typeConference paperen
dc.identifier.rmid0030043411en
dc.contributor.conferenceIEEE International Conference on Image Processing (ICIP) (27 Sep 2015 - 30 Sep 2015 : Quebec City, CANADA)en
dc.identifier.doi10.1109/ICIP.2015.7351238en
dc.relation.granthttp://purl.org/au-research/grants/arc/CE140100016en
dc.identifier.pubid235620-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS06en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidCarneiro, G. [0000-0002-5571-6220]en
Appears in Collections:Computer Science publications

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