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dc.contributor.authorZheng, Chaojie
dc.contributor.authorWang, Xiuying
dc.contributor.authorFeng, Dagan
dc.date.accessioned2019-06-14T05:44:31Z
dc.date.available2019-06-14T05:44:31Z
dc.date.issued2015-11-05
dc.identifier.citationC. Zheng, X. Wang and D. Feng, "A statistical method for lung tumor segmentation uncertainty in PET images based on user inference," 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 2015, pp. 2255-2258. doi: 10.1109/EMBC.2015.7318841en_AU
dc.identifier.issn1094-687X
dc.identifier.urihttp://hdl.handle.net/2123/20553
dc.description.abstractPET has been widely accepted as an effective imaging modality for lung tumor diagnosis and treatment. However, standard criteria for delineating tumor boundary from PET are yet to develop largely due to relatively low quality of PET images, uncertain tumor boundary definition, and variety of tumor characteristics. In this paper, we propose a statistical solution to segmentation uncertainty on the basis of user inference. We firstly define the uncertainty segmentation band on the basis of segmentation probability map constructed from Random Walks (RW) algorithm; and then based on the extracted features of the user inference, we use Principle Component Analysis (PCA) to formulate the statistical model for labeling the uncertainty band. We validated our method on 10 lung PET-CT phantom studies from the public RIDER collections [1] and 16 clinical PET studies where tumors were manually delineated by two experienced radiologists. The methods were validated using Dice similarity coefficient (DSC) to measure the spatial volume overlap. Our method achieved an average DSC of 0.878±0.078 on phantom studies and 0.835±0.039 on clinical studies.en_AU
dc.publisherIEEEen_AU
dc.relationARC DP140100211
dc.rights© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_AU
dc.titleA statistical method for lung tumor segmentation uncertainty in PET images based on user inferenceen_AU
dc.typeConference paperen_AU
dc.type.pubtypePost-printen_AU


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