TY - JOUR
T1 - A Rapid, Accurate and Machine-agnostic Segmentation and Quantification Method for CT-based COVID-19 Diagnosis
AU - Zhou, Longxi
AU - Li, Zhongxiao
AU - Zhou, Juexiao
AU - Li, Haoyang
AU - Chen, Yupeng
AU - Huang, Yuxin
AU - Xie, Dexuan
AU - Zhao, Lintao
AU - Fan, Ming
AU - Hashmi, Shahrukh
AU - AbdelKareem, Faisal
AU - Eiada, Riham
AU - Xiao, Xigang
AU - Li, Lihua
AU - Qiu, Zhaowen
AU - Gao, Xin
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank Jiayu Zang, Weihang Song, Fengyao Zhu and Yi Zhao for their help on data preparation, annotation and transfer.
PY - 2020
Y1 - 2020
N2 - COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid detection has been mainly used as the gold standard to confirm this RNA virus-based disease, it has been shown that such a strategy has a high false negative rate, especially for patients in the early stage, and thus CT imaging has been applied as a major diagnostic modality in confirming positive COVID-19. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the stateof-the-art segmentation method requires a high level of human intervention. In this paper, we propose a fullyautomatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon two innovations: 1) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients’ data measured at different time points, which greatly alleviates the data scarcity issue; and 2) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an.
AB - COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid detection has been mainly used as the gold standard to confirm this RNA virus-based disease, it has been shown that such a strategy has a high false negative rate, especially for patients in the early stage, and thus CT imaging has been applied as a major diagnostic modality in confirming positive COVID-19. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the stateof-the-art segmentation method requires a high level of human intervention. In this paper, we propose a fullyautomatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon two innovations: 1) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients’ data measured at different time points, which greatly alleviates the data scarcity issue; and 2) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an.
UR - http://hdl.handle.net/10754/663534
UR - https://ieeexplore.ieee.org/document/9115057/
U2 - 10.1109/TMI.2020.3001810
DO - 10.1109/TMI.2020.3001810
M3 - Article
C2 - 32730214
SN - 1558-254X
SP - 1
EP - 1
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
ER -
