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

Nimrod Sagie, Shiri Almog, Ayelet Talby, Hayit Greenspan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationMedical Imaging 2021
Subtitle of host publicationComputer-Aided Diagnosis
EditorsMaciej A. Mazurowski, Karen Drukker
ISBN (Electronic)9781510640238
StatePublished - 2021
EventMedical Imaging 2021: Computer-Aided Diagnosis - Virtual, Online, United States
Duration: 15 Feb 202119 Feb 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2021: Computer-Aided Diagnosis
Country/TerritoryUnited States
CityVirtual, Online


  • AI
  • COVID-19
  • Chest CT
  • Deep Learning
  • Lung
  • Transfer Learning


Dive into the research topics of 'COVID-19 opacity segmentation in chest CT via HydraNet: A joint learning multi-decoder network'. Together they form a unique fingerprint.

Cite this