A Lightweight Swin Transformer-based pipeline for Optical Coherence Tomography Image Denoising in Skin Application

Jinpeng Liao, Chunhui Li (Lead / Corresponding author), Zhihong Huang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
43 Downloads (Pure)

Abstract

Optical coherence tomography (OCT) has attracted attention in dermatology applications for skin disease characterization and diagnosis because it provides high-resolution (<10 μm) of tissue non-invasively with high imaging speed (2–8 s). However, the quality of OCT images can be significantly degraded by speckle noise, which results from light waves scattering in multiple directions. This noise can hinder the accuracy of disease diagnosis, and the conventional frame averaging method requires multiple repeated (e.g., four to six) scans, which is time consuming and introduces motion artifacts. To overcome these limitations, we proposed a lightweight U-shape Swin (LUSwin) transformer-based denoising pipeline to recover high-quality OCT images from the noisy OCT images by utilizing a fast one-repeated OCT scan. In terms of the peak signal-to-noise-ratio (PSNR) performance, the results reveal that the denoised images from the LUSwin transformer (26.92) are of a higher quality than the four-repeated frame-averaging method (26.19). Compared to the state-of-the-art networks in image denoising, the proposed LUSwin transformer has the smallest floating points operation (3.9299 G) and has the second highest PSNR results, only 0.02 lower than the Swin-UNet, which has the highest PSNR results (26.94). This study demonstrates that the transformer model has the capacity to denoise the noisy OCT image from a fast one-repeated OCT scan.
Original languageEnglish
Article number468
Number of pages18
JournalTomography Image Denoising in Skin Application
Volume10
Issue number4
DOIs
Publication statusPublished - 19 Apr 2023

Keywords

  • optical coherence tomography (OCT)
  • image denoising
  • deep learning

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