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Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

PURPOSE: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods...

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Detaylı Bibliyografya
Yayımlandı:Med Phys
Asıl Yazarlar: Park, Sang Hyun, Gao, Yaozong, Shi, Yinghuan, Shen, Dinggang
Materyal Türü: Artigo
Dil:Inglês
Baskı/Yayın Bilgisi: American Association of Physicists in Medicine 2014
Konular:
Online Erişim:https://ncbi.nlm.nih.gov/pmc/articles/PMC4224685/
https://ncbi.nlm.nih.gov/pubmed/25370629
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1118/1.4898200
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