Deep learning model for segmentation of trabecular tissue on CT data of the lumbar spine

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but.event.date23.04.2024cs
but.event.titleSTUDENT EEICT 2024cs
dc.contributor.authorNagyová, Miriam
dc.contributor.authorNohel, Michal
dc.date.accessioned2024-07-09T07:47:48Z
dc.date.available2024-07-09T07:47:48Z
dc.date.issued2024cs
dc.description.abstractThis paper focuses on training a deep learning model for vertebral body segmentation of the lumbar spine. The nnU-Net model was trained and tested on a publicly available dataset LumVBCanSeg consisting of 185 lumbar CT scans. Dice coefficient was used to evaluate the accuracy of the trained model. The mean Dice coefficient of the testing dataset was 0.949 with a standard deviation of 0.103. The model was also tested on clinical data containing various abnormalities, such as lytic lesions in multiple myeloma patients and metallic implants. Results were evaluated visually. While the model showed high accuracy on the testing dataset, the results on scans with anomalies showed a decline in accuracy.en
dc.formattextcs
dc.format.extent8-11cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers. s. 8-11. ISBN 978-80-214-6230-4cs
dc.identifier.doi10.13164/eeict.2024.8
dc.identifier.isbn978-80-214-6230-4
dc.identifier.issn2788-1334
dc.identifier.urihttps://hdl.handle.net/11012/249286
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 30st Conference STUDENT EEICT 2024: Selected papersen
dc.relation.urihttps://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdfcs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectmultiple myelomaen
dc.subjectosteolytic lesionsen
dc.subjectnnU-Neten
dc.subjectsegmentationen
dc.titleDeep learning model for segmentation of trabecular tissue on CT data of the lumbar spineen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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