Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography

Jiang Z, Huang Z, Qiu B, Meng X, You Y, Liu X, Geng M, Liu G, Zhou C, Yang K, Maier A, Ren Q, Lu Y (2021)


Publication Type: Journal article

Publication year: 2021

Journal

Book Volume: 40

Pages Range: 688-698

Article Number: 9246597

Journal Issue: 2

DOI: 10.1109/TMI.2020.3035154

Abstract

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.

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APA:

Jiang, Z., Huang, Z., Qiu, B., Meng, X., You, Y., Liu, X.,... Lu, Y. (2021). Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography. IEEE Transactions on Medical Imaging, 40(2), 688-698. https://dx.doi.org/10.1109/TMI.2020.3035154

MLA:

Jiang, Zhe, et al. "Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography." IEEE Transactions on Medical Imaging 40.2 (2021): 688-698.

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