[口头报告]Automatic cone-beam computed tomography Segmentation with small samples based on Generative Adversarial Networks and semantic segmentation - 报告详情

Automatic cone-beam computed tomography Segmentation with small samples based on Generative Adversarial Networks and semantic segmentation
编号:8 访问权限:仅限参会人 更新:2021-10-30 07:01:24 浏览:124次 口头报告

报告开始:2021年11月13日 16:15 (Asia/Shanghai)

报告时间:15min

所在会议:[PS1] Plenary Session 1 [OR1] Workshop on Oral Radiology

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摘要
This paper establishes a method to realize semi-automatic or automatic labeling of multi-dimensional data based on small samples and weak labeling. This method could effectively assist dentist in segmentation of different tissues. Based on the U-net combined with the Generative Adversarial Networks method, segmentation can be realized on multi-dimensional data. It also includes three-dimensional mesh reconstruction of the segmented tissue, smooth the boundary, and the result data can be used to aid clinical diagnosis and print. The result of segmentation can reflect the structural distribution of different tissues, and effectively build a mechanical model based on Cone-beam computed tomography systems (CBCT) datasets.
关键字
segmentation,Generative Adversarial Networks,annotation,CBCT
报告人
慧芳 杨
工程师 北京大学口腔医院

稿件作者
慧芳 杨 北京大学口腔医院
刚 李 北京大学口腔医院
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重要日期

摘要提交日期:

2021/08/31

2021/10/25

全文投稿日期:  

2021/09/15

2021/10/25

录取通知日期: 

2021/09/30

2021/11/01

会议日期:   2021-11-12-2021-11-14

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