TY - GEN
T1 - COVID-19 opacity segmentation in chest CT via HydraNet
T2 - Medical Imaging 2021: Computer-Aided Diagnosis
AU - Sagie, Nimrod
AU - Almog, Shiri
AU - Talby, Ayelet
AU - Greenspan, Hayit
N1 - Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2021
Y1 - 2021
N2 - The outbreak of the coronavirus and its rapid spread was recently acknowledged as a worldwide pandemic. Chest CT scans show high potential for detecting pathological manifestations. Hence, the demand for computer-aided tools to support radiologists has grown exponentially. In this work, we developed a deep learning based algorithm, with an emphasis on novel transfer learning methods, to segment COVID-19 opacity in chest CT scans. Our method focuses on creating a deep encoder for feature extraction by using a Fully Convolutional Network (FCN) architecture with one shared encoder and N task-related decoders, named HydraNet. The HydraNet architecture allowed the leverage of a large variety of medical datasets from different domains, in order to achieve better performances on a limited dataset. We achieved a dice score, sensitivity, and precision of 0.724, 0.75, and 0.807 respectively, on the test set, which is competitive with known state-of-the-art results.
AB - The outbreak of the coronavirus and its rapid spread was recently acknowledged as a worldwide pandemic. Chest CT scans show high potential for detecting pathological manifestations. Hence, the demand for computer-aided tools to support radiologists has grown exponentially. In this work, we developed a deep learning based algorithm, with an emphasis on novel transfer learning methods, to segment COVID-19 opacity in chest CT scans. Our method focuses on creating a deep encoder for feature extraction by using a Fully Convolutional Network (FCN) architecture with one shared encoder and N task-related decoders, named HydraNet. The HydraNet architecture allowed the leverage of a large variety of medical datasets from different domains, in order to achieve better performances on a limited dataset. We achieved a dice score, sensitivity, and precision of 0.724, 0.75, and 0.807 respectively, on the test set, which is competitive with known state-of-the-art results.
KW - AI
KW - COVID-19
KW - Chest CT
KW - Deep Learning
KW - Lung
KW - Transfer Learning
UR - http://www.scopus.com/inward/record.url?scp=85103688968&partnerID=8YFLogxK
U2 - 10.1117/12.2581111
DO - 10.1117/12.2581111
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AN - SCOPUS:85103688968
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2021
A2 - Mazurowski, Maciej A.
A2 - Drukker, Karen
PB - SPIE
Y2 - 15 February 2021 through 19 February 2021
ER -