Focal-balanced attention u-net with dynamic thresholding by spatial regression for segmentation of aortic dissection in CT imagery

Tsung Han Lee, Li Ting Huang, Paul Kuo, Chien Kuo Wang, Jiun In Guo

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

An aortic dissection has been reported a mortality of 50% within the first 48 hours and an increase of 1-2% per hour. Therefore, rapid diagnosis of intimal flap would be very important for the emergency treatment of patients. In order to accurately present the affected part of AD and reduce the time for doctors to diagnose, image segmentation is the most effective way of presentation. We used the U-Net model in this study and focus on AD (including ascending, arch, and descending part) in the detection process. Furthermore, we design the site and area regression (SAR) module. With this help of accurate prediction, we achieved slice-level sensitivity and specificity of 99.1 % and 93.2%, respectively.

原文English
主出版物標題2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
發行者IEEE Computer Society
頁面541-544
頁數4
ISBN(電子)9781665412469
DOIs
出版狀態Published - 2021 4月 13
事件18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
持續時間: 2021 4月 132021 4月 16

出版系列

名字Proceedings - International Symposium on Biomedical Imaging
2021-April
ISSN(列印)1945-7928
ISSN(電子)1945-8452

Conference

Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
國家/地區France
城市Nice
期間21-04-1321-04-16

All Science Journal Classification (ASJC) codes

  • 生物醫學工程
  • 放射學、核子醫學和影像學

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