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

2018 ◽  
Vol 59 (1) ◽  
pp. 63 ◽  
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

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

2018 ◽  
Vol 9 (7) ◽  
pp. 3244 ◽  
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

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

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Sripad Krishna Devalla ◽  
Giridhar Subramanian ◽  
Tan Hung Pham ◽  
Xiaofei Wang ◽  
Shamira Perera ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Optic Nerve ◽  
Optic Nerve Head ◽  
Similarity Index ◽  
Optical Coherence ◽  
Patient Discomfort ◽  
Single Frame ◽  
The Mean ◽  
Noise Ratio

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.

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

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

Abstract 15219: Fully Automated Calcification Segmentation in Intravascular Optical Coherence Tomography Images Using a Two-step Deep Learning Approach

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Juhwan Lee ◽  
Yazan Gharaibeh ◽  
Vladislav N Zimin ◽  
Luis A Dallan ◽  
Hiram G Bezerra ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Learning Approach ◽  
Optical Coherence ◽  
Stent Deployment ◽  
Automated Method ◽  
Increased Risk ◽  
Maximum Angle ◽  
Intravascular Optical Coherence Tomography ◽  
Sensitivity Specificity

Introduction: Major calcifications are of great concern when performing percutaneous coronary intervention as they hinder stent deployment. Calcifications can lead to under-expansion and strut malapposition, with increased risk of thrombosis and in-stent restenosis. Therefore, accurate identification, visualization, and quantification of calcifications are important. Objective: In this study, we developed a 2-step deep learning approach to enable segmentation of major calcifications in a typical 500+ frame intravascular optical coherence tomography (IVOCT) images. Methods: The dataset consisted of a total of 12,551 IVOCT frames across 68 patients with 68 pullbacks. We applied a series of pre-processing steps including guidewire/shadow removal, lumen detection, pixel shifting, and Gaussian filtering. To detect the major calcifications in step 1, we implemented the 3D convolutional neural network consisting of 5 convolutional, 5 max-pooling, and 2 fully-connected layers. In step-2, SegNet deep learning model was used to segment calcified plaques. In both steps, classification errors were reduced using conditional random field. Results: Step-1 reliably identified major calcifications (sensitivity/specificity: 97.7%/87.7%). Semantic segmentation of calcifications following step-2 was typically visually quite good (Fig. 1) with (sensitivity/specificity: 86.2%/96.7%). Our method was superior to a single step approach and showed excellent reproducibility on repetitive IVOCT pullbacks, with very small differences of clinically relevant attributes (maximum angle, maximum thickness, and length) and the exact same IVOCT calcium scores for assessment of stent deployment. Conclusions: We developed the fully-automated method for identifying calcifications in IVOCT images based on a 2-step deep learning approach. Extensive analyses indicate that our method is very informative for both live-time treatment planning and research purposes.

scholarly journals Optic Nerve Head Measurements With Optical Coherence Tomography: A Phantom-Based Study Reveals Differences Among Clinical Devices

2016 ◽  
Vol 57 (9) ◽  
pp. OCT413 ◽  
Author(s):  
Anant Agrawal ◽  
Jigesh Baxi ◽  
William Calhoun ◽  
Chieh-Li Chen ◽  
Hiroshi Ishikawa ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Optic Nerve ◽  
Optic Nerve Head ◽  
Optical Coherence

scholarly journals In vivo analysis of glaucoma-related features within the optic nerve head using enhanced depth imaging optical coherence tomography

PLoS ONE ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. e0180128 ◽  
Author(s):  
Tiago S. Prata ◽  
Flavio S. Lopes ◽  
Vitor G. Prado ◽  
Izabela Almeida ◽  
Igor Matsubara ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Optic Nerve ◽  
Optic Nerve Head ◽  
Enhanced Depth Imaging ◽  
Optical Coherence ◽  
Depth Imaging ◽  
In Vivo Analysis ◽  
Imaging Optical Coherence Tomography

scholarly journals Optical coherence tomography in papilledema and pseudopapilledema with and without optic nerve head drusen

2014 ◽  
Vol 62 (12) ◽  
pp. 1146 ◽  
Author(s):  
ShikhaTalwar Bassi ◽  
KuppuswamyParthasarthy Mohana
Keyword(s):  
Optical Coherence Tomography ◽  
Optic Nerve ◽  
Optic Nerve Head ◽  
Optical Coherence ◽  
Optic Nerve Head Drusen

Comparison of Spectral Domain Optical Coherence Tomography and Color Photographic Imaging of the Optic Nerve Head in Management of Glaucoma

2009 ◽  
Vol 40 (3) ◽  
pp. 255-263 ◽  
Author(s):  
Yuriko Kotera ◽  
Yoshiaki Yasuno ◽  
Masanori Hangai ◽  
Ryo Inoue ◽  
Shuichi Makita ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Optic Nerve ◽  
Optic Nerve Head ◽  
Spectral Domain ◽  
Optical Coherence
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