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dc.contributor.authorDandin, Ozgüren_US
dc.contributor.authorTeomete, Uygaren_US
dc.contributor.authorOsman, Onuren_US
dc.contributor.authorTulum, Gökalpen_US
dc.contributor.authorErgin, Tunceren_US
dc.contributor.authorSabuncuoğlu, Mehmet Zaferen_US
dc.date.accessioned2017-07-25T10:57:35Z
dc.date.available2017-07-25T10:57:35Z
dc.date.issued2016
dc.identifier.citationDandin, O., Teomete, U., Osman, O., Tulum, G., Ergin, T., Sabuncuoğlu, M. Z. (2016). Automated segmentation of the injured spleen. International Journal Of Computer Assisted Radiology And Surgery. 11. 3, 351- 368.en_US
dc.identifier.issn1861-6429
dc.identifier.urihttps://hdl.handle.net/20.500.12294/867
dc.identifier.urihttp://dx.doi.org/10.1007/s11548-015-1288-9
dc.descriptionOsman, Onur (Arel Author)en_US
dc.description.abstractPurpose: To develop a novel automated method for segmentation of the injured spleen using morphological properties following abdominal trauma. Average attenuation of a normal spleen in computed tomography (CT) does not vary significantly between subjects. However, in the case of solid organ injury, the shape and attenuation of the spleen on CT may vary depending on the time and severity of the injury. Timely assessment of the severity and extent of the injury is of vital importance in the setting of trauma. Methods: We developed an automated computer-aided method for segmenting the injured spleen from CT scans of patients who had splenectomy due to abdominal trauma. We used ten subjects to train our computer-aided diagnosis (CAD) method. To validate the CAD method, we used twenty subjects in our testing group. Probabilistic atlases of the spleens were created using manually segmented data from ten CT scans. The organ location was modeled based on the position of the spleen with respect to the left side of the spine followed by the extraction of shape features. We performed the spleen segmentation in three steps. First, we created a mask of the spleen, and then we used this mask to segment the spleen. The third and final step was the estimation of the spleen edges in the presence of an injury such as laceration or hematoma. Results: The traumatized spleens were segmented with a high degree of agreement with the radiologist-drawn contours. The spleen quantification led to [Formula: see text] volume overlap, [Formula: see text] Dice similarity index, [Formula: see text] precision/sensitivity, [Formula: see text] volume estimation error rate, [Formula: see text] average surface distance/root-mean-squared error. Conclusions: Our CAD method robustly segments the spleen in the presence of morphological changes such as laceration, contusion, pseudoaneurysm, active bleeding, periorgan and parenchymal hematoma, including subcapsular hematoma due to abdominal trauma. CAD of the splenic injury due to abdominal trauma can assist in rapid diagnosis and assessment and guide clinical management. Our segmentation method is a general framework that can be adapted to segment other injured solid abdominal organs.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTraumaen_US
dc.subjectSolid Organen_US
dc.subjectDiagnosisen_US
dc.subjectComputeren_US
dc.titleAutomated segmentation of the injured spleenen_US
dc.typearticleen_US
dc.relation.journalInternational Journal Of Computer Assisted Radiology And Surgeryen_US
dc.contributor.authorIDTR13219en_US
dc.contributor.authorIDTR160901en_US
dc.contributor.authorIDTR167300en_US
dc.identifier.volume11en_US
dc.identifier.issue3en_US
dc.identifier.startpage351en_US
dc.identifier.endpage368en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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