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Enhanced Denoising of Optical Coherence Tomography Images Using Residual U-Net

arXiv 2407.13090 ↗First posted 18 Jul 2024eess.IV cs.CVcited by 0

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Abstract (arXiv)

Optical Coherence Tomography (OCT) imaging is pivotal in diagnosing ophthalmic conditions by providing detailed cross-sectional images of the anterior and posterior segments of the eye. Nonetheless, speckle noise and other imaging artifacts inherent to OCT impede the accuracy of diagnosis significantly. In this study, we proposed an enhanced denoising model using a Residual U-Net architecture that effectively diminishes noise and improves image clarity across both Anterior Segment OCT (ASOCT) and polarization-sensitive OCT (PSOCT) images. Our approach demonstrated substantial improvements in image quality metrics: the Peak Signal Noise Ratio (PSNR) was 34.343 ± 1.113 for PSOCT images, and Structural Similarity Index Measure (SSIM) values were 0.885 ± 0.030, indicating enhanced preservation of tissue integrity and textural details. For ASOCT images, we observed the PSNR to be 23.525 ± 0.872 dB and SSIM 0.407 ± 0.044, reflecting significant enhancements in visual quality and structural accuracy. These metrics substantiate the models efficacy in not only reducing noise but also in maintaining crucial anatomical features, thereby enabling more precise and efficient clinical evaluations. The dual functionality across both ASOCT and PSOCT modalities underscores the versatility and potential for broad application in clinical settings, optimizing diagnostic processes and reducing the necessity for prolonged imaging sessions.

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References · 12

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  4. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  5. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  6. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  7. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
  8. 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
  9. 2018A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Headcited by 11
  10. 2020Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligencecited by 10
  11. 2023Real-World Denoising via Diffusion Modelcited by 10
  12. 2020OCT-GAN: Single Step Shadow and Noise Removal from Optical Coherence Tomography Images of the Human Optic Nerve Headcited by 4

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