Multi-task generative adversarial network for retinal optical coherence tomography image denoising

光学相干层析成像 计算机科学 散斑噪声 人工智能 降噪 计算机视觉 分割 噪音(视频) 任务(项目管理) 模式识别(心理学) 斑点图案 图像(数学) 医学 眼科 经济 管理
作者
Qiaoxue Xie,Zongqing Ma,Lianqing Zhu,Fan Fan,Xiaochen Meng,Xinxiao Gao,Jiang Zhu
出处
期刊:Physics in Medicine and Biology [IOP Publishing]
卷期号:68 (4): 045002-045002 被引量:7
标识
DOI:10.1088/1361-6560/ac944a
摘要

Objective. Optical coherence tomography (OCT) has become an essential imaging modality for the assessment of ophthalmic diseases. However, speckle noise in OCT images obscures subtle but important morphological details and hampers its clinical applications. In this work, a novel multi-task generative adversarial network (MGAN) is proposed for retinal OCT image denoising.Approach. To strengthen the preservation of retinal structural information in the OCT denoising procedure, the proposed MGAN integrates adversarial learning and multi-task learning. Specifically, the generator of MGAN simultaneously undertakes two tasks, including the denoising task and the segmentation task. The segmentation task aims at the generation of the retinal segmentation map, which can guide the denoising task to focus on the retina-related region based on the retina-attention module. In doing so, the denoising task can enhance the attention to the retinal region and subsequently protect the structural detail based on the supervision of the structural similarity index measure loss.Main results. The proposed MGAN was evaluated and analyzed on three public OCT datasets. The qualitative and quantitative comparisons show that the MGAN method can achieve higher image quality, and is more effective in both speckle noise reduction and structural information preservation than previous denoising methods.Significance. We have presented a MGAN for retinal OCT image denoising. The proposed method provides an effective way to strengthen the preservation of structural information while suppressing speckle noise, and can promote the OCT applications in the clinical observation and diagnosis of retinopathy.
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