Localization of diagnostically relevant regions of interest in whole slide images: a comparative study

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dc.citation.epage506en_US
dc.citation.issueNumber4en_US
dc.citation.spage496en_US
dc.citation.volumeNumber29en_US
dc.contributor.authorMercan, E.en_US
dc.contributor.authorAksoy, S.en_US
dc.contributor.authorShapiro, L. G.en_US
dc.contributor.authorWeaver, D. L.en_US
dc.contributor.authorBrunyé, T. T.en_US
dc.contributor.authorElmore, J. G.en_US
dc.date.accessioned2018-04-12T10:57:34Z
dc.date.available2018-04-12T10:57:34Z
dc.date.issued2016-08en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractWhole slide digital imaging technology enables researchers to study pathologists’ interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists’ actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors. © 2016, Society for Imaging Informatics in Medicine.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T10:57:34Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016en
dc.identifier.doi10.1007/s10278-016-9873-1en_US
dc.identifier.issn0897-1889
dc.identifier.urihttp://hdl.handle.net/11693/36927
dc.language.isoEnglishen_US
dc.publisherSpringer New York LLCen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s10278-016-9873-1en_US
dc.source.titleJournal of Digital Imagingen_US
dc.subjectComputer visionen_US
dc.subjectDigital pathologyen_US
dc.subjectMedical image analysisen_US
dc.subjectRegion of interesten_US
dc.subjectWhole slide imagingen_US
dc.subjectBiopsyen_US
dc.subjectDecision makingen_US
dc.subjectDiagnosisen_US
dc.subjectImage processingen_US
dc.subjectImage segmentationen_US
dc.subjectImaging techniquesen_US
dc.subjectInformation retrievalen_US
dc.subjectMedical imagingen_US
dc.subjectMedicineen_US
dc.subjectColor and texture featuresen_US
dc.subjectComparative studiesen_US
dc.subjectDigital-imaging technologyen_US
dc.subjectLogistic regression classifieren_US
dc.subjectMedical decision makingen_US
dc.subjectComputer graphicsen_US
dc.subjectBreasten_US
dc.subjectFemaleen_US
dc.subjectHumansen_US
dc.subjectLogistic Modelsen_US
dc.subjectMammographyen_US
dc.subjectMedical Errorsen_US
dc.subjectPathologistsen_US
dc.titleLocalization of diagnostically relevant regions of interest in whole slide images: a comparative studyen_US
dc.typeArticleen_US

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