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dc.contributor.authorSaygılı, Ahmet
dc.contributor.authorAlbayrak, Songül
dc.date.accessioned2022-05-11T14:15:57Z
dc.date.available2022-05-11T14:15:57Z
dc.date.issued2020
dc.identifier.issn1573-4056
dc.identifier.issn1875-6603
dc.identifier.urihttps://doi.org/10.2174/1573405614666181017122109
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6129
dc.description.abstractBackground: Automatic diagnostic systems in medical imaging provide useful information to support radiologists and other relevant experts. The systems that help radiologists in their analysis and diagnosis appear to be increasing. Discussion: Knee joints are intensively studied structures, as well. In this review, studies that automatically segment meniscal structures from the knee joint MR images and detect tears have been investigated. Some of the studies in the literature merely perform meniscus segmentation, while others include classification procedures that detect both meniscus segmentation and anomalies on menisci. The studies performed on the meniscus were categorized according to the methods they used. The methods used and the results obtained from such studies were analyzed along with their drawbacks, and the aspects to be developed were also emphasized. Conclusion: The work that has been done in this area can effectively support the decisions that will be made by radiology and orthopedics specialists. Furthermore, these operations, which were performed manually on MR images, can be performed in a shorter time with the help of computer-aided systems, which enables early diagnosis and treatment.en_US
dc.description.sponsorshipTurkish Scientific and Technical Research Council-TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [116E151]en_US
dc.description.sponsorshipThis study was supported by Turkish Scientific and Technical Research Council-TUBITAK (Project Number: 116E151).en_US
dc.language.isoengen_US
dc.publisherBentham Science Publ Ltden_US
dc.identifier.doi10.2174/1573405614666181017122109
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectKnee jointen_US
dc.subjectCADen_US
dc.subjectsegmentationen_US
dc.subjectmeniscusen_US
dc.subjecttear detectionen_US
dc.subjectMRIen_US
dc.subjectmedical imageen_US
dc.subjectBrain-Tumor Segmentationen_US
dc.subjectMagnetic-Resonance Imagesen_US
dc.subjectBreast-Cancer Detectionen_US
dc.subjectMarkov Random-Fielden_US
dc.subjectRegion-Growing Algorithmen_US
dc.subjectAutomated Segmentationen_US
dc.subjectBone Segmentationen_US
dc.subjectNeural-Networksen_US
dc.subjectCartilageen_US
dc.subjectClassificationen_US
dc.titleKnee Meniscus Segmentation and Tear Detection from MRI: A Reviewen_US
dc.typereviewen_US
dc.relation.ispartofCurrent Medical Imagingen_US
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.authorid0000-0001-8625-4842
dc.authorid0000-0002-1786-6869
dc.identifier.volume16en_US
dc.identifier.issue1en_US
dc.identifier.startpage2en_US
dc.identifier.endpage15en_US
dc.institutionauthorSaygılı, Ahmet
dc.relation.publicationcategoryDiğeren_US
dc.authorscopusid55807379700
dc.authorscopusid16309030500
dc.authorwosidSAYGILI, AHMET/AAG-4161-2019
dc.authorwosidVarlı, Songül/AAZ-4672-2020
dc.identifier.wosWOS:000505777700002en_US
dc.identifier.scopus2-s2.0-85078311428en_US
dc.identifier.pmid31989889en_US


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