A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

Abstract Optical coherence tomography (OCT) has become an established clinical routine for the in vivo imaging of the optic nerve head (ONH) tissues, that is crucial in the diagnosis and management of various ocular and neuro-ocular pathologies. However, the presence of speckle noise affects the quality of OCT images and its interpretation. Although recent frame-averaging techniques have shown to enhance OCT image quality, they require longer scanning durations, resulting in patient discomfort. Using a custom deep learning network trained with 2,328 ‘clean B-scans’ (multi-frame B-scans; signal averaged), and their corresponding ‘noisy B-scans’ (clean B-scans + Gaussian noise), we were able to successfully denoise 1,552 unseen single-frame (without signal averaging) B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean signal to noise ratio (SNR) increased from 4.02 ± 0.68 dB (single-frame) to 8.14 ± 1.03 dB (denoised). For all the ONH tissues, the mean contrast to noise ratio (CNR) increased from 3.50 ± 0.56 (single-frame) to 7.63 ± 1.81 (denoised). The mean structural similarity index (MSSIM) increased from 0.13 ± 0.02 (single frame) to 0.65 ± 0.03 (denoised) when compared with the corresponding multi-frame B-scans. Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort.

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Longitudinal assessment of optic nerve head changes using optical coherence tomography in a primate microbead model of ocular hypertension

Scientific Reports ◽

10.1038/s41598-020-71555-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Anita S. Y. Chan ◽

Tin Aung Tun ◽

John C. Allen ◽

Myoe Naing Lynn ◽

Sai Bo Bo Tun ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Optic Nerve ◽

Ocular Hypertension ◽

Optic Nerve Head ◽

Structural Damage ◽

Structural Changes ◽

Lamina Cribrosa ◽

Optical Coherence ◽

Longitudinal Assessment ◽

The Mean

Abstract In humans, the longitudinal characterisation of early optic nerve head (ONH) damage in ocular hypertension (OHT) is difficult as patients with glaucoma usually have structural ONH damage at the time of diagnosis. Previous studies assessed glaucomatous ONH cupping by measuring the anterior lamina cribrosa depth (LCD) and minimal rim width (MRW) using optical coherence tomography (OCT). In this study, we induced OHT by repeated intracameral microbead injections in 16 cynomolgus primates (10 unilateral; 6 bilateral) and assessed the structural changes of the ONH longitudinally to observe early changes. Elevated intraocular pressure (IOP) in OHT eyes was maintained for 7 months and serial OCT measurements were performed during this period. The mean IOP was significantly elevated in OHT eyes when compared to baseline and compared to the control eyes. Thinner MRW and deeper LCD values from baseline were observed in OHT eyes with the greatest changes seen between month 1 and month 2 of OHT. Both the mean and maximum IOP values were significant predictors of MRW and LCD changes, although the maximum IOP was a slightly better predictor. We believe that this model could be useful to study IOP-induced early ONH structural damage which is important for understanding glaucoma pathogenesis.

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DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images

Biomedical Optics Express ◽

10.1364/boe.9.003244 ◽

2018 ◽

Vol 9 (7) ◽

pp. 3244 ◽

Cited By ~ 38

Author(s):

Sripad Krishna Devalla ◽

Prajwal K. Renukanand ◽

Bharathwaj K. Sreedhar ◽

Giridhar Subramanian ◽

Liang Zhang ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Optic Nerve ◽

Optic Nerve Head ◽

Optical Coherence ◽

Learning Network ◽

Deep Learning Network

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Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning

Biomedical Optics Express ◽

10.1364/boe.395934 ◽

2020 ◽

Vol 11 (11) ◽

pp. 6356

Author(s):

Sripad Krishna Devalla ◽

Tan Hung Pham ◽

Satish Kumar Panda ◽

Liang Zhang ◽

Giridhar Subramanian ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Optic Nerve ◽

Optic Nerve Head ◽

Optical Coherence ◽

Label Free ◽

3D Segmentation

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A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head

Investigative Opthalmology & Visual Science ◽

10.1167/iovs.17-22617 ◽

2018 ◽

Vol 59 (1) ◽

pp. 63 ◽

Cited By ~ 39

Author(s):

Sripad Krishna Devalla ◽

Khai Sing Chin ◽

Jean-Martial Mari ◽

Tin A. Tun ◽

Nicholas G. Strouthidis ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Optic Nerve ◽

Optic Nerve Head ◽

Learning Approach ◽

Optical Coherence

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Optical coherence tomography in the setting of optic nerve head cupping reversal in secondary childhood glaucoma

Journal of American Association for Pediatric Ophthalmology and Strabismus ◽

10.1016/j.jaapos.2021.03.003 ◽

2021 ◽

Author(s):

Abdelrahman M. Elhusseiny ◽

Deborah K. VanderVeen

Keyword(s):

Optical Coherence Tomography ◽

Optic Nerve ◽

Optic Nerve Head ◽

Optical Coherence

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Synthetic polarization-sensitive optical coherence tomography by deep learning

npj Digital Medicine ◽

10.1038/s41746-021-00475-8 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Yi Sun ◽

Jianfeng Wang ◽

Jindou Shi ◽

Stephen A. Boppart

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Imaging System ◽

Imaging Modality ◽

Similarity Index ◽

Polarization Sensitivity ◽

Imaging Method ◽

Optical Coherence ◽

The Real ◽

Oct Imaging

AbstractPolarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

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