IEEE Trans Med Imaging. 2021 Feb;40(2):688-698. doi: 10.1109/TMI.2020.3035154. Epub 2021 Feb 2.
Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Deep learning networks have been widely applied in the field of OCTA reconstruction, benefiting from its powerful mapping capability among images. However, these existing deep learning-based methods depend on high-quality labels, which are hard to acquire considering imaging hardware limitations and practical data acquisition conditions. In this article, we proposed an unprecedented weakly supervised deep learning-based pipeline for OCTA reconstruction task, in the absence of high-quality training labels. The proposed pipeline was investigated on an in vivo animal dataset and a human eye dataset by a cross-validation strategy. Compared with supervised learning approaches, the proposed approach demonstrated similar or even better performance in the OCTA reconstruction task. These investigations indicate that the proposed weakly supervised learning strategy is well capable of performing OCTA reconstruction, and has a certain potential towards clinical applications.
光学相干断层扫描血管造影术（OCTA）是一种很有前途的微血管成像方式。深度学习网络已广泛应用于 OCTA 重建领域，得益于其在图像之间具有强大的映射能力。然而，这些现有的基于深度学习的方法依赖于高质量的标签，考虑到成像硬件的限制和实际的数据采集条件，这些标签很难获取。在本文中，我们提出了一种前所未有的基于弱监督深度学习的 OCTA 重建方法，在缺乏高质量训练标签的情况下使用该方法。通过交叉验证策略，我们在活体动物数据集和人眼数据集上对所提出的管道进行了研究。与监督学习方法相比，所提出的方法在 OCTA 重建任务中表现出相似甚至更好的性能。这些研究表明，所提出的弱监督学习策略能够很好地执行 OCTA 重建，并且在临床应用方面具有一定的潜力。
