Semantic denoising autoencoders for retinal optical coherence tomography
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Noise in speckle-prone optical coherence tomography tends to obfuscate important details necessary for medical diagnosis. In this paper, a denoising approach that preserves disease characteristics on retinal optical coherence tomography images in ophthalmology is presented. By combining a deep convolutional autoencoder with a priorly trained ResNet image classifier as regularizer, the perceptibility of delicate details is encouraged and only information-less background noise is filtered out. With our approach, higher peak signal-to-noise ratios with PSNR = 31.2 dB and higher classification accuracy of ACC = 85.0 % can be achieved for denoised images compared to state-of-the-art denoising with PSNR = 29.4 dB or ACC = 70.3 %, depending on the method. It is shown that regularized autoencoders are capable of denoising retinal OCT images without blurring details of diseases.
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