Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model

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dc.contributor.authorTurk, Fuat
dc.contributor.authorLuy, Murat
dc.contributor.authorBarisci, Necaattin
dc.date.accessioned2021-01-14T18:10:23Z
dc.date.available2021-01-14T18:10:23Z
dc.date.issued2020
dc.departmentKKÜ
dc.description.abstractKidney tumors represent a type of cancer that people of advanced age are more likely to develop. For this reason, it is important to exercise caution and provide diagnostic tests in the later stages of life. Medical imaging and deep learning methods are becoming increasingly attractive in this sense. Developing deep learning models to help physicians identify tumors with successful segmentation is of great importance. However, not many successful systems exist for soft tissue organs, such as the kidneys and the prostate, of which segmentation is relatively difficult. In such cases where segmentation is difficult, V-Net-based models are mostly used. This paper proposes a new hybrid model using the superior features of existing V-Net models. The model represents a more successful system with improvements in the encoder and decoder phases not previously applied. We believe that this new hybrid V-Net model could help the majority of physicians, particularly those focused on kidney and kidney tumor segmentation. The proposed model showed better performance in segmentation than existing imaging models and can be easily integrated into all systems due to its flexible structure and applicability. The hybrid V-Net model exhibited average Dice coefficients of 97.7% and 86.5% for kidney and tumor segmentation, respectively, and, therefore, could be used as a reliable method for soft tissue organ segmentation.en_US
dc.identifier.citationTürk F, Lüy M, Barışçı N. Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model. Mathematics. 2020; 8(10):1772.en_US
dc.identifier.doi10.3390/math8101772
dc.identifier.issn2227-7390
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85092907611
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/math8101772
dc.identifier.urihttps://hdl.handle.net/20.500.12587/12557
dc.identifier.volume8en_US
dc.identifier.wosWOS:000586920900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMDPIen_US
dc.relation.ispartofMATHEMATICS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectmedical image segmentationen_US
dc.subjectrenal segmentationen_US
dc.subjectcomputed tomographyen_US
dc.subjectkidney canceren_US
dc.subjecthybrid V-Net modelen_US
dc.titleKidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Modelen_US
dc.typeArticle

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