Abstract

We compared the segmentation quality obtained with the following network architectures: Unet, Wide Unet, Unet++ and MultiResUnet. The MultiResUnet architecture has shown improved quality in other medical image segmentation tasks. It is applied to the problem of left ventricular segmentation for the first time. This architecture showed segmentation accuracy on augmented data equal to 92.78% by Dice metric. This is 1.3% more than the result of Unet++ and 2.5% more than the result of Unet. Also, this architecture showed a smaller variance of segmentation accuracy on cross validation: 1.2% vs. 1.4%.

Original languageEnglish
Title of host publicationSIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages775-779
Number of pages5
ISBN (Electronic)9781728144016
DOIs
Publication statusPublished - Oct 2019
Event2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019 - Novosibirsk, Russian Federation
Duration: 21 Oct 201927 Oct 2019

Publication series

NameSIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings

Conference

Conference2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019
CountryRussian Federation
CityNovosibirsk
Period21/10/201927/10/2019

Keywords

  • deep learning
  • EchoCG images
  • left ventricle
  • MultiResUNet
  • segmentation
  • Unet

ASJC Scopus subject areas

  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems and Management
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Instrumentation
  • Computer Networks and Communications

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