Modified U-net with Different Attention Mechanisms for Acute Ischemic Stroke Segmentation using Non-Contrast CT

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Abstract

The instant diagnosis of acute ischemic stroke using non-contrast computed tomography brain scans is important for right decision upon a treatment. Artificial intelligence and deep learning tools can assist a radiology specialist in analysis and interpretation of CT images. This work aims at improving U-net model and testing it on real non-contrast CT images of acute ischemic stroke. In this work, we use the following attention modules to learn a better feature representation and for more accurate segmentation: Convolutional Block Attention Module on skip-connection stage, double attention gates on decoding stage, and Feature Pyramid Attention as bottleneck. Experiments were conducted using a combination of the Binary Cross-Entropy Loss and Dice Loss as the loss function, and separately with the Focal Tversky Loss. An anonymized sample of 500 patients with ischemic stroke was obtained from International Tomography Center SB RAS. After verification, 25 patients were used in our study. The application of the considered architecture in 2D ischemic stroke segmentation was quite successful.

Original languageEnglish
Title of host publicationProceedings - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages133-136
Number of pages4
ISBN (Electronic)9781728176918
DOIs
Publication statusPublished - 13 May 2021
Event2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021 - Yekaterinburg, Russian Federation
Duration: 13 May 202114 May 2021

Publication series

NameProceedings - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021

Conference

Conference2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021
CountryRussian Federation
CityYekaterinburg
Period13.05.202114.05.2021

Keywords

  • acute stroke
  • attention
  • CBAM
  • deep neural network
  • texture segmentation
  • U-net

OECD FOS+WOS

  • 1.02 COMPUTER AND INFORMATION SCIENCES
  • 2.06.IG ENGINEERING, BIOMEDICAL
  • 2.02.IQ ENGINEERING, ELECTRICAL & ELECTRONIC

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