scholarly journals Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography

2019 ◽  
Vol 178 ◽  
pp. 181-189 ◽  
Author(s):  
Oscar Perdomo ◽  
Hernán Rios ◽  
Francisco J. Rodríguez ◽  
Sebastián Otálora ◽  
Fabrice Meriaudeau ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Learning Approach ◽  
Retinal Diseases ◽  
Optical Coherence

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 A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis

2021 ◽  
Author(s):  
Fangyao Tang ◽  
Xi Wang ◽  
An-ran Ran ◽  
Carmen KM Chan ◽  
Mary Ho ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Vision Loss ◽  
Three Dimensional ◽  
Learning System ◽  
Optical Coherence ◽  
Automated Classification

<a><b>Objective:</b></a> Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep-learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices. <p><b>Research Design and Methods:</b> We trained and validated two versions of a multi-task convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume-scans and two-dimensional (2D) B-scans respectively. For both 3D and 2D CNNs, we employed the residual network (ResNet) as the backbone. For the 3D CNN, we used a 3D version of ResNet-34 with the last fully connected layer removed as the feature extraction module. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent datasets from Singapore, Hong Kong, the US, China, and Australia. </p> <p><b>Results:</b> In classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920–0.954), 0.958 (0.930–0.977), and 0.965 (0.948–0.977) for primary dataset obtained from Cirrus, Spectralis, and Triton OCTs respectively, in addition to AUROCs greater than 0.906 for the external datasets. For the further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940–0.995), 0.951 (0.898–0.982), and 0.975 (0.947–0.991) for the primary dataset and greater than 0.894 for the external datasets. </p> <p><b>Conclusion:</b> We demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which may potentially create a more effective triaging mechanism to eye clinics. </p>

scholarly journals Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases

2018 ◽  
Vol 9 (7) ◽  
pp. 3092 ◽  
Author(s):  
Acner Camino ◽  
Zhuo Wang ◽  
Jie Wang ◽  
Mark E. Pennesi ◽  
Paul Yang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Retinal Diseases ◽  
Optical Coherence ◽  
En Face

scholarly journals DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images

2020 ◽  
Vol 9 (2) ◽  
pp. 23
Author(s):  
Haris Cheong ◽  
Sripad Krishna Devalla ◽  
Tan Hung Pham ◽  
Liang Zhang ◽  
Tin Aung Tun ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Learning Approach ◽  
Optical Coherence
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