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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Pages541-544
Number of pages4
ISBN (Electronic)9781665412469
DOIs
Publication statusPublished - 2021 Apr 13
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: 2021 Apr 132021 Apr 16

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2021-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Country/TerritoryFrance
CityNice
Period21-04-1321-04-16

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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