@inproceedings{2070403b0e4c4dde955336a0db9d02a6,
title = "Focal-balanced attention u-net with dynamic thresholding by spatial regression for segmentation of aortic dissection in CT imagery",
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. ",
keywords = "Aortic dissection, Deep learning, Imbalance dataset, Segmentation, U-Net",
author = "Lee, {Tsung Han} and Huang, {Li Ting} and Paul Kuo and Wang, {Chien Kuo} and Guo, {Jiun In}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 ; Conference date: 13-04-2021 Through 16-04-2021",
year = "2021",
month = apr,
day = "13",
doi = "10.1109/ISBI48211.2021.9434028",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "541--544",
booktitle = "2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021",
address = "United States",
}