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dc.contributor.authorTeomete, U.en_US
dc.contributor.authorTulum, Gökalpen_US
dc.contributor.authorErgin, T.en_US
dc.contributor.authorCüce, F.en_US
dc.contributor.authorKöksal, M.en_US
dc.contributor.authorDandin, O.en_US
dc.contributor.authorOsman, Onuren_US
dc.date.accessioned2019-06-24T09:23:52Z
dc.date.available2019-06-24T09:23:52Z
dc.date.issued2019en_US
dc.identifier.citationTeomete, U., Tulum, G., Ergin, T., Cuce, F., Koksal, M., Dandin, O., & Osman, O. (2018). Automated computer-aided diagnosis of splenic lesions due to abdominal trauma. Hippokratia, 22(2), 80-85.en_US
dc.identifier.issn1108-4189
dc.identifier.urihttps://hdl.handle.net/20.500.12294/1500
dc.descriptionTulum, Gökalp (Arel Author), Osman, Onur (Arel Author)en_US
dc.description.abstractBackground: Computer-aided detection in the setting of trauma presents unique challenges due to variations in shape and attenuation of the injured organs based on the timing and severity of the injury. We developed and validated an automated computer-aided diagnosis algorithm to detect splenic lesions such as laceration, contusion, subcapsular hematoma, perisplenic hematoma, and active extravasation using computed tomography (CT) images in patients sustaining blunt or penetrating abdominal trauma. Methods: We categorized the splenic pathologies into three groups: contusion/laceration, hematoma, and active extravasation. We first analyzed the spleen and perisplenic region by estimating the mean value and standard deviation of the spleen. We determined adaptive threshold values based on the histogram of the area and detected the lesions after morphological operations and volumetric comparisons. Results: The overall performance of the three computer-aided diagnosis (CAD) algorithms is an accuracy of 0.80, sensitivity of 0.95, specificity of 0.67, and a diagnostic odds ratio (DOR) of 40 with a 95 % confidence interval (CI): 14 to 117. The CAD of perisplenic hematoma had the highest diagnosis rates with an accuracy of 0.90, a sensitivity of 0.95, specificity of 0.80, and DOR of 76 with a 95 % CI: 13 to 442. Conclusions: We developed a new algorithm to detect post-traumatic splenic lesions automatically and with high accuracy. Our method could potentially lead to the automated diagnosis of all traumatic abdominal pathologies.en_US
dc.language.isoengen_US
dc.publisherLithographiaen_US
dc.relation.ispartofHippokratiaen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTraumaen_US
dc.subjectSolid Organ Injuriesen_US
dc.subjectComputer-Aided Diagnosisen_US
dc.titleAutomated computer-aided diagnosis of splenic lesions due to abdominal traumaen_US
dc.typearticleen_US
dc.departmentMühendislik ve Mimarlık Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.authoridhttps://orcid.org/0000-0003-2865-5781en_US
dc.authoridhttps://orcid.org/0000-0001-7675-7999en_US
dc.identifier.volume22en_US
dc.identifier.issue2en_US
dc.identifier.startpage80en_US
dc.identifier.endpage85en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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