Dilated-residual U-Net for optical coherence tomography noise reduction and resolution improvement

Xinyang He; Zhengyu Qiao; Yong Huang; Qun Hao

doi:10.1117/12.2687055

27 November 2023 Dilated-residual U-Net for optical coherence tomography noise reduction and resolution improvement

Xinyang He, Zhengyu Qiao, Yong Huang, Qun Hao

Proceedings Volume 12770, Optics in Health Care and Biomedical Optics XIII; 127702B (2023) https://doi.org/10.1117/12.2687055
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

Optical coherence tomography (OCT) is a non-invasive 3D imaging technique that provides high-resolution images and has been extensively used in biomedical research and clinical studies. Although micrometer resolution is already considered high for biological tissue imaging, the need for even higher resolution remains constant. Improving the resolution of OCT images can reveal previously unseen microstructures, which can aid in achieving more accurate diagnoses. Currently, the resolution of OCT images is primarily constrained by speckle noise and spectral bandwidth limitations. We have achieved simultaneous suppression of speckle noise and resolution improvement in OCT images in our previous work. However, traditional methods based on prior optimization iteration have a high computational cost, which limits its applicability. In this paper, we propose an improved deep learning model called DRUNET (Dilated Residual U-Net) to achieve noise reduction and resolution improvement simultaneously. The model incorporates dilated convolution and residual learning to enhance the learning capacity of the U-Net. In addition, we apply a simple, yet effective attention module called Convolutional Block Attention Module (CBAM) to improve DRUNET performance. We evaluate the performance of the DRUNET model in denoising and improving resolution on two types of OCT images. The experimental results demonstrate the effectiveness of the proposed model, which enables us to batch process poor-quality OCT images quickly without requiring any parameter fine-tuning under time constraints.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinyang He, Zhengyu Qiao, Yong Huang, and Qun Hao "Dilated-residual U-Net for optical coherence tomography noise reduction and resolution improvement", Proc. SPIE 12770, Optics in Health Care and Biomedical Optics XIII, 127702B (27 November 2023); https://doi.org/10.1117/12.2687055

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