OCT Denoising Performance Comparison on 2D and 1D Approaches

Abstract

Purpose : Current Optical Coherence Tomography (OCT) denoising techniques mainly focus on denoising 2-dimensional (2D) B-scans, especially for deep learning (DL) methods, which assume spatial integrity among neighboring samplings. However, OCT signal is essentially one dimensional (1D), and eye movements during scanning often violate the assumption. The purpose of this study was to see if 1D denoising is a feasible approach for clinical OCT imaging.

Methods : 3D SD-OCT data within 6x6x2mm volumes centered on optic nerve head were obtained from 121 eyes (79 patients). Clean reference scans were constructed by registering and averaging 6 OCT scans obtained on the same day using ANTs software. As shown in Figure 1, we used a 5-layer U-shape net (UNet) for both 2D denoiser (Figure 1.(a)) and 1D denoiser (Figure1.(b)). In addition, both 2D and 1D approaches are combined by using the 2D denoised B-scan as a mask to selectively remove high signal peaks in the 1D denoised B-scan (Figure 1.(c)). Peak signal-to-noise ratio (PSNR) and contrast-to-noise ratio (CNR) were calculated to compare the performance.

Results : Subjectively, the 2D denoiser generated smoother edges but tended to over-smooth textual information within the retinal layers, while the 1D denoiser preserved more textual information but caused more jittering at retinal edges due to the lack of structural information from neighboring A-scans. Quantitatively, the 1D denoiser showed similar PSNR to the 2D denoiser, while outperforming in CNR (PSNR: 31.20 (1D) V.S. 31.20 dB (2D), p=0.963; CNR: 4.23 (1D) V.S. 3.90 dB (2D), p<0.001, paired t-test, Table 1). The combined denoiser further improved CNR (4.39 (combined) V.S. 3.90 dB (2D), p<0.001). Combining 1D and 2D denoisers, the denoised B-scan showed more continuous edges compared to the 1D denoiser and did not lose the texture information compared to the 2D denoiser (Figure 2).

Conclusions : Quantitatively, 1D denoiser performance is as good as 2D denoiser or even better. A combination of 1D and 2D approaches may provide well-balanced image enhancement in clinical applications.

This is a 2021 ARVO Annual Meeting abstract.