SiameseGAN: A Generative Model for Denoising of Spectral Domain Optical Coherence Tomography Images

Kande, NA and Dakhane, R and Dukkipati, A and Yalavarthy, PK (2021) SiameseGAN: A Generative Model for Denoising of Spectral Domain Optical Coherence Tomography Images. In: IEEE Transactions on Medical Imaging, 40 (1). pp. 180-192.

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Abstract

Optical coherence tomography (OCT) is a standard diagnostic imaging method for assessment of ophthalmic diseases. The speckle noise present in the high-speed OCT images hampers its clinical utility, especially in Spectral-Domain Optical Coherence Tomography (SDOCT). In this work, a new deep generative model, called as SiameseGAN, for denoising Low signal-to-noise ratio (LSNR) B-scans of SDOCT has been developed. SiameseGAN is a Generative Adversarial Network (GAN) equipped with a siamese twin network. The siamese network module of the proposed SiameseGAN model helps the generator to generate denoised images that are closer to groundtruth images in the feature space, while the discriminator helps in making sure they are realistic images. This approach, unlike baseline dictionary learning technique (MSBTD), does not require an apriori high-quality image from the target imaging subject for denoising and takes less time for denoising. Moreover, various deep learning models that have been shown to be effective in performing denoising task in the SDOCT imaging were also deployed in this work. A qualitative and quantitative comparison on the performance of proposed method with these state-of-the-art denoising algorithms has been performed. The experimental results show that the speckle noise can be effectively mitigated using the proposed SiameseGAN along with faster denoising unlike existing approaches. © 1982-2012 IEEE.

Item Type:	Journal Article
Publication:	IEEE Transactions on Medical Imaging
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright of this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Coherent light; Deep learning; Diagnosis; Learning systems; Signal to noise ratio; Speckle; Strain measurement; Tomography, Adversarial networks; De-noising algorithm; Dictionary learning; High quality images; Low signal-to-noise ratio; Quantitative comparison; Spectral domain optical coherence tomographies; Spectral-domain optical coherence tomography, Optical tomography
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	28 Jan 2021 05:38
Last Modified:	28 Jan 2021 05:38
URI:	http://eprints.iisc.ac.in/id/eprint/67683

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