Variational Formulation of Unsupervised Deep Learning for Ultrasound Image Artifact Removal
Recently, deep learning approaches have been successfully used for ultrasound (US) image artifact removal. However, paired high-quality images for supervised training are difficult to obtain in many practical situations. Inspired by the recent theory of unsupervised learning using optimal transport...
| Veröffentlicht in: | IEEE transactions on ultrasonics, ferroelectrics, and frequency control. - 1986. - 68(2021), 6 vom: 01. Juni, Seite 2086-2100 |
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| Weitere Verfasser: | , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2021
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on ultrasonics, ferroelectrics, and frequency control |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't |
| Zusammenfassung: | Recently, deep learning approaches have been successfully used for ultrasound (US) image artifact removal. However, paired high-quality images for supervised training are difficult to obtain in many practical situations. Inspired by the recent theory of unsupervised learning using optimal transport driven CycleGAN (OT-CycleGAN), here, we investigate the applicability of unsupervised deep learning for US artifact removal problems without matched reference data. Two types of OT-CycleGAN approaches are employed: one with the partial knowledge of the image degradation physics and the other with the lack of such knowledge. Various US artifact removal problems are then addressed using the two types of OT-CycleGAN. Experimental results for various unsupervised US artifact removal tasks confirmed that our unsupervised learning method delivers results comparable to supervised learning in many practical applications |
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| Beschreibung: | Date Completed 25.10.2021 Date Revised 25.10.2021 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1525-8955 |
| DOI: | 10.1109/TUFFC.2021.3056197 |