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Published VersionCC BY V4.0, Open Access
Journal article
Deep feature loss to denoise OCT images using deep neural networks
Journal of Biomedical Optics, Vol.26(4), pp.1-18
2021
PMID: 33893726
Abstract
Significance: Speckle noise is an inherent limitation of optical coherence tomography (OCT) images that makes clinical interpretation challenging. The recent emergence of deep learning could offer a reliable method to reduce noise in OCT images.
Aim: We sought to investigate the use of deep features (VGG) to limit the effect of blurriness and increase perceptual sharpness and to evaluate its impact on the performance of OCT image denoising (DnCNN).
Approach: Fifty-one macula-centered OCT pairs were used in training of the network. Another set of 20 OCT pair was used for testing. The DnCNN model was cascaded with a VGG network that acted as a perceptual loss function instead of the traditional losses of L1 and L2. The VGG network remains fixed during the training process. We focused on the individual layers of the VGG-16 network to decipher the contribution of each distinctive layer as a loss function to produce denoised OCT images that were perceptually sharp and that preserved the faint features (retinal layer boundaries) essential for interpretation. The peak signal-to-noise ratio (PSNR), edge-preserving index, and no-reference image sharpness/blurriness [perceptual sharpness index (PSI), just noticeable blur (JNB), and spectral and spatial sharpness measure (S3)] metrics were used to compare deep feature losses with the traditional losses.
Results: The deep feature loss produced images with high perceptual sharpness measures at the cost of less smoothness (PSNR) in OCT images. The deep feature loss outperformed the traditional losses (L1 and L2) for all of the evaluation metrics except for PSNR. The PSI, S3, and JNB estimates of deep feature loss performance were 0.31, 0.30, and 16.53, respectively. For L1 and L2 losses performance, the PSI, S3, and JNB were 0.21 and 0.21, 0.17 and 0.16, and 14.46 and 14.34, respectively.
Conclusions: We demonstrate the potential of deep feature loss in denoising OCT images. Our preliminary findings suggest research directions for further investigation.
Details
- Title
- Deep feature loss to denoise OCT images using deep neural networks
- Authors
- Maryam Mehdizadeh (Author) - Australian e-Health Research CentreCara MacNish (Author) - The University of Western AustraliaDi Xiao (Author) - Australian e-Health Research CentreDavid Alonso-Caneiro (Author) - Queensland University of TechnologyJason Kugelman (Author) - Queensland University of TechnologyMohammed Bennamoun (Author) - The University of Western Australia
- Publication details
- Journal of Biomedical Optics, Vol.26(4), pp.1-18
- Publisher
- S P I E - International Society for Optical Engineering
- Date published
- 2021
- DOI
- 10.1117/1.JBO.26.4.046003
- ISSN
- 1560-2281; 1083-3668
- PMID
- 33893726
- Copyright note
- © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JBO.26.4.046003]
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99973593202621
- Output Type
- Journal article
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20 Record Views
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- Collaboration types
- Domestic collaboration
- Web Of Science research areas
- Biochemical Research Methods
- Optics
- Radiology, Nuclear Medicine & Medical Imaging