DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Minyoung | ko |
dc.contributor.author | Park, Jinah | ko |
dc.date.accessioned | 2022-04-24T00:00:40Z | - |
dc.date.available | 2022-04-24T00:00:40Z | - |
dc.date.created | 2022-04-21 | - |
dc.date.created | 2022-04-21 | - |
dc.date.created | 2022-04-21 | - |
dc.date.created | 2022-04-21 | - |
dc.date.issued | 2022-03 | - |
dc.identifier.citation | Journal of Computing Science and Engineering, v.16, no.1, pp.43 - 51 | - |
dc.identifier.issn | 1976-4677 | - |
dc.identifier.uri | http://hdl.handle.net/10203/295852 | - |
dc.description.abstract | Learning-based medical image segmentation has been advanced with the collection of datasets and various training methodologies. In this work, we target bone cement (polymethylmethacrylate [PMMA]) inserted vertebral body segmentation, where the target dataset was relatively scarce, compared to a large-scale dataset for the regular vertebra segmentation task. We presented a novel domain transformation framework, where a large-scale training set for our target task was generated from the existing dataset of a different domain. We proposed two main components: label translation and image translation. Label translation was proposed to filter out unnecessary regions in a segmentation map for our target task. In addition to label translation, image translation was proposed to virtually generate PMMA-inserted images from the original data. The synthesized training set by our method successfully simulated the data distribution of the target task; therefore a clear performance improvement was observed by the dataset. By extensive experiments, we showed that our method outperformed baseline methods in terms of segmentation performance. In addition, a more accurate shape and volume of a bone were measured by our method, which satisfied the medical purpose of segmentation. | - |
dc.language | English | - |
dc.publisher | Korean Institute of Information Scientists and Engineers | - |
dc.title | Domain Transformation for PMMA-Inserted Vertebral Body Segmentation | - |
dc.type | Article | - |
dc.identifier.scopusid | 2-s2.0-85129746993 | - |
dc.type.rims | ART | - |
dc.citation.volume | 16 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 43 | - |
dc.citation.endingpage | 51 | - |
dc.citation.publicationname | Journal of Computing Science and Engineering | - |
dc.identifier.doi | 10.5626/jcse.2022.16.1.43 | - |
dc.contributor.localauthor | Park, Jinah | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Computer vision | - |
dc.subject.keywordAuthor | Domain transformation | - |
dc.subject.keywordAuthor | Medical image segmentation | - |
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