DHNet: High‐resolution and hierarchical network for cross‐domain OCT speckle noise reduction

• Published in: Medical physics (Lancaster) 2022-09, Vol.49 (9), p.5914-5928
• Main Authors: Zhou, Yi, Li, Jiang, Wang, Meng, Peng, Yuanyuan, Chen, Zhongyue, Zhu, Weifang, Shi, Fei, Wang, Lianyu, Wang, Tingting, Yao, Chenpu, Chen, Xinjian
• Format: Article
• Language: English
• Subjects: domain adaptation; generative adversarial networks; OCT speckle noise reduction
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.15712

Purpose Optical coherence tomography (OCT) imaging uses the principle of Michelson interferometry to obtain high‐resolution images by coherent superposing of multiple forward and backward scattered light waves with random phases. This process inevitably produces speckle noise that severely compromises visual quality of OCT images and degrades performances of subsequent image analysis tasks. In addition, datasets obtained by different OCT scanners have distribution shifts, making a speckle noise suppression model difficult to be generalized across multiple datasets. In order to solve the above issues, we propose a novel end‐to‐end denoising framework for OCT images collected by different scanners. Methods The proposed model utilizes the high‐resolution network (HRNet) as backbone for image restoration, which reconstructs high‐fidelity images by maintaining high‐resolution representations throughout the entire learning process. To compensate distribution shifts among datasets collected by different scanners, we develop a hierarchical adversarial learning strategy for domain adaption. The proposed model is trained using datasets with clean ground truth produced by two commercial OCT scanners, and then applied to suppress speckle noise in OCT images collected by our recently developed OCT scanner, BV‐1000 (China Bigvision Corporation). We name the proposed model as DHNet (Double‐H‐Net, High‐resolution and Hierarchical Network). Results We compare DHNet with state‐of‐the‐art methods and experiment results show that DHNet improves signal‐to‐noise ratio by a large margin of 18.137 dB as compared to the best of our previous method. In addition, DHNet achieves a testing time of 25 ms, which satisfies the real‐time processing requirement for the BV‐1000 scanner. We also conduct retinal layer segmentation experiment on OCT images before and after denoising and show that DHNet can also improve segmentation. Conclusions The proposed DHNet can compensate domain shifts between different data sets while significantly improve speckle noise suppression. The HRNet backbone is utilized to carry low‐ and high‐resolution information to recover fidelity images. Domain adaptation is achieved by a hierarchical module through adversarial learning. In addition, DHNet achieved a testing time of 25 ms, which satisfied the real‐time processing requirement.