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Deep feature loss to denoise OCT images using deep neural networks.

Maryam Mehdizadeh1,2,3, Cara MacNish2, Di Xiao1

  • 1Australian e-Health Research Ctr., Australia.

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Deep learning effectively reduces speckle noise in Optical Coherence Tomography (OCT) images, enhancing perceptual sharpness. This deep feature loss method outperforms traditional losses for denoising OCT scans.

Keywords:
convolutional neural networksimage enhancementimage processingoptical coherence tomographyspeckle

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Speckle noise in Optical Coherence Tomography (OCT) images hinders clinical interpretation.
  • Deep learning presents a promising approach for noise reduction in medical imaging.

Purpose of the Study:

  • To investigate the efficacy of deep features (VGG) for reducing blurriness and enhancing perceptual sharpness in OCT images.
  • To evaluate the impact of deep feature loss on the performance of OCT image denoising (DnCNN) compared to traditional methods.

Main Methods:

  • A DnCNN model was combined with a fixed VGG network as a perceptual loss function, replacing traditional L1 and L2 losses.
  • The VGG-16 network's individual layers were analyzed for their contribution to denoising and preserving fine details in OCT images.
  • Performance was evaluated using Peak Signal-to-Noise Ratio (PSNR), edge-preserving index, and perceptual sharpness metrics (PSI, JNB, S3).

Main Results:

  • Deep feature loss significantly improved perceptual sharpness (PSI, S3, JNB) compared to L1 and L2 losses.
  • While deep feature loss yielded higher perceptual sharpness, it resulted in slightly lower smoothness (PSNR).
  • The deep feature loss method outperformed traditional losses across all evaluated metrics except for PSNR.

Conclusions:

  • Deep feature loss shows significant potential for denoising OCT images, improving perceptual sharpness.
  • This approach offers a viable alternative to traditional denoising methods for OCT imaging.
  • Further research is warranted to explore the full capabilities and applications of deep feature loss in OCT image analysis.