Deep convolutional robust PCA with application to ultrasound imaging

Regev Cohen, Yi Zhang, Oren Solomon, Daniel Toberman, Liran Taieb, Ruud J.G. van Sloun, Yonina C. Eldar

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

6 Citations (Scopus)
1 Downloads (Pure)

Abstract

Sparse and low-rank decomposition, also known as robust principle component analysis, has been applied successfully in numerous applications. Typically, this approach leads to a minimization problem which is solved using iterative algorithms. Drawing inspiration from recurrent networks, in recent years deep-learning strategies have been extended to mimic the behavior of iterative algorithms, with reduced complexity. In this work, we propose an extension of these deep architectures to robust principle component analysis in which fully-connected layers are replaced with convolutional ones. This strategy offers spatial invariance and significant reduction in the number of learned parameters. We then apply the proposed method to contrast-enhanced ultrasound, in which low-rank tissue signal needs to be removed in order to visualize blood vessels. We demonstrate the effectiveness of our approach on simulations and in-vivo rat brain scans. The resulting images exhibit improved visual quality and contrast compared with images obtained by commonly practiced methods.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages3212-3216
Number of pages5
ISBN (Electronic)978-1-4799-8131-1
DOIs
Publication statusPublished - May 2019
Event2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019) - Brighton Conference Centre, Brighton, United Kingdom
Duration: 12 May 201917 May 2019
https://2019.ieeeicassp.org/

Conference

Conference2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019)
Abbreviated titleICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/1917/05/19
Internet address

Keywords

  • Contrast agents
  • Deep learning
  • Low-rank decomposition
  • Ultrasound
  • Unfolding

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