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Automatic segmentation of head anatomical structures from sparsely-annotated images

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dc.contributor.authorSugino, T-
dc.contributor.authorRoth, HR-
dc.contributor.authorEshghi, M-
dc.contributor.authorOda, M-
dc.contributor.authorChung, MS-
dc.contributor.authorMori, K-
dc.date.accessioned2019-12-10T06:53:51Z-
dc.date.available2019-12-10T06:53:51Z-
dc.date.issued2018-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/17828-
dc.description.abstractBionic humanoid systems, which are elaborate human models with sensors, have been developed as a tool for quantitative evaluation of doctors' psychomotor skills and medical device performances. For creation of the elaborate human models, this study presents automated segmentation of head sectioned images using sparsely-annotated data based on deep convolutional neural network. We applied the following fully convolutional networks (FCNs) to the sparse-annotation-based segmentation: a standard FCN and a dilated convolution based FCN. To validate the availability of FCNs for segmentation of head structures from sparse annotation, we performed 8- and 243-label segmentation experiments using different two sets of head sectioned images in the Visible Korean Human project. In the segmentation experiments, only 10% of all images in each data set were used for training data. Both of the FCNs could achieve the mean segmentation accuracy of more than 85% in the 8-label segmentation. In the 243-label segmentation, though the mean segmentation accuracy was about 50%, the results suggested that the FCNs, especially the dilated convolution based FCNs, had potential to achieve accurate segmentation of anatomical structures, except for small-sized and complex-shaped tissues, even from sparse annotation.-
dc.formatapplication/pdf-
dc.language.isoen-
dc.titleAutomatic segmentation of head anatomical structures from sparsely-annotated images-
dc.typeArticle-
dc.subject.keywordsemantic segmentation-
dc.subject.keywordconvolutional neural networks-
dc.subject.keywordsparse annotation-
dc.subject.keyworddilated convolution-
dc.subject.keyworddeep learning-
dc.contributor.affiliatedAuthor정, 민석-
dc.type.localJournal Papers-
dc.identifier.doi10.1109/CBS.2017.8266085-
dc.citation.title2017 IEEE International Conference on Cyborg and Bionic Systems (CBS)-
dc.citation.volume2017-
dc.citation.date2018-
dc.citation.startPage145-
dc.citation.endPage149-
dc.identifier.bibliographicCitation2017 IEEE International Conference on Cyborg and Bionic Systems (CBS), 2017. : 145-149, 2018-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.relation.journalidJJ00000002-
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Journal Papers > School of Medicine / Graduate School of Medicine > Anatomy
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