[口头报告]The Segmentation of Knee MR Image Using Nested Deep Network and Attention Mechanism - 报告详情

The Segmentation of Knee MR Image Using Nested Deep Network and Attention Mechanism
编号:62 访问权限:仅限参会人 更新:2021-11-10 09:34:50 浏览:138次 口头报告

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

报告时间:15min

所在会议:[PS1] Plenary Session 1 [AI1] Workshop on AI

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摘要
AbstractPurpose: Magnetic resonance imaging is of great significance in clinical diagnosis of knee joint. Constructing knee model based on the segmentation of MR images is important for many applications, especially the local specific absorption rate estimation which needs electromagnetic simulation.
Methods and Materials: In this paper,we proposed a method using a nested deep network, U-Net++, and attention mechanism to strengthen the segmentation effect of the tissues. The residual module was used to enhance the convergence ability of the network. In addition, Multi-scale deep supervision was performed to preserve the rich semantic features of the decoder paths. Furthermore, we also used a multiclass classification guided module to reduce false positives in image segmentation and improve the overall accuracy of image segmentation.
Results and Discussion: Compared with current mainstream methods, the proposed method achieved better performance of tissue segmentation on the dataset of T1-weighted sagittal images collected by our laboratory, especially for cartilage and meniscus.
Conclusion: The method integrating U-Net++ and attention mechanism is hopeful to be used to construct knee model for local specific absorption rate estimation.
关键字
knee joint,MRI,image segmentation,U-Net++,attention mechanism
报告人
涵之 张
研究生 Beijing University of Chemical and Technology

Hanzhi Zhang is a postgraduate student at the School of Information Science and Technology, Beijing University of Chemical Technology. His main research interests are deep learning and medical image segmentation.

稿件作者
涵之 张 Beijing University of Chemical Technology
藏菊 邢 Beijing University of Chemical Technology
亮 肖 Beijing University of Chemical Technology
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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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