COVID-19 opacity segmentation in chest CT via HydraNet: a joint learning multi-decoder network

Nimrod Sagie; Shiri Almog; Ayelet Talby; Hayit Greenspan

doi:10.1117/12.2581111

15 February 2021 COVID-19 opacity segmentation in chest CT via HydraNet: a joint learning multi-decoder network

Nimrod Sagie, Shiri Almog, Ayelet Talby, Hayit Greenspan

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115971U (2021) https://doi.org/10.1117/12.2581111
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Nimrod Sagie, Shiri Almog, Ayelet Talby, and Hayit Greenspan "COVID-19 opacity segmentation in chest CT via HydraNet: a joint learning multi-decoder network", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115971U (15 February 2021); https://doi.org/10.1117/12.2581111

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available