Variational Formulation of Unsupervised Deep Learning for Ultrasound Image Artifact Removal.

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Abstract

Recently, deep learning approaches have been successfully used for ultrasound 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 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, the other with the lack of such knowledge. Various US artifacts removal problem 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.