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

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.

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Multi-label classification of Retinal Disorders in Optical Coherence Tomography using Deep Learning

10.1109/icesc51422.2021.9532711 ◽

2021 ◽

Author(s):

Abhinav Sharma ◽

Akshay Vijay Khanna ◽

Muskaan Bhargava

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Optical Coherence ◽

Retinal Disorders

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A deep learning approach to predict visual field using optical coherence tomography

PLoS ONE ◽

10.1371/journal.pone.0234902 ◽

2020 ◽

Vol 15 (7) ◽

pp. e0234902

Author(s):

Keunheung Park ◽

Jinmi Kim ◽

Jiwoong Lee

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Visual Field ◽

Learning Approach ◽

Optical Coherence

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Deep learning for classification of breast cancer in optical coherence tomography (OCT) imaging (Conference Presentation)

Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVIII ◽

10.1117/12.2546256 ◽

2020 ◽

Author(s):

Rohan Bareja ◽

Diana Mojahed ◽

Christine P. Hendon

Keyword(s):

Breast Cancer ◽

Optical Coherence Tomography ◽

Deep Learning ◽

Optical Coherence ◽

Oct Imaging

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Detection and Classification of Retinal Diseases in Spectral Domain Optical Coherence Tomography Images Using Texture Analysis of Macula

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2017.6756 ◽

2017 ◽

Vol 14 (9) ◽

pp. 4424-4428

Author(s):

P Nandhini ◽

S Prakash

Keyword(s):

Optical Coherence Tomography ◽

Texture Analysis ◽

Spectral Domain ◽

Retinal Diseases ◽

Optical Coherence

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

10.2337/figshare.14710284.v1 ◽

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>Objective:</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. Research Design and Methods: 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. Results: 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. Conclusion: 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.

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