Deep learning algorithm for generating optical coherence tomography angiography (OCTA) maps of the retinal vasculature

2020 ◽  
Author(s):  
Pei Lin Li ◽  
Céline O'Neil ◽  
Samin Saberi ◽  
Kenneth Sinder ◽  
Kathleen X. Wang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Optical Coherence Tomography Angiography ◽  
Learning Algorithm ◽  
Retinal Vasculature ◽  
Optical Coherence ◽  
Deep Learning Algorithm

scholarly journals Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans

2020 ◽  
Vol 138 (4) ◽  
pp. 333 ◽  
Author(s):  
Atalie C. Thompson ◽  
Alessandro A. Jammal ◽  
Samuel I. Berchuck ◽  
Eduardo B. Mariottoni ◽  
Felipe A. Medeiros
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Optical Coherence ◽  
Deep Learning Algorithm

scholarly journals Identification of Coronary Calcifications in Optical Coherence Tomography Imaging Using Deep Learning

2020 ◽  
Author(s):  
Yarden Avital ◽  
Akiva Madar ◽  
Shlomi Arnon ◽  
Edward Koifman
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Image Acquisition ◽  
Percutaneous Treatment ◽  
Optical Coherence ◽  
Optimal Method ◽  
Deep Learning Algorithm ◽  
Coronary Calcifications ◽  
Artery Disease

Abstract Coronary calcifications are an obstacle for successful percutaneous treatment of coronary artery disease patients. The optimal method for delineating calcifications extent is optical coherence tomography (coronary OCT). To identify calcification on OCT and subsequently tailor the appropriate treatment, requires expertise in both image acquisition and interpretation. Image acquisition consists from system calibration, blood clearance by a contrast agent along with synchronization of the pullback process. Accurate interpretation demands careful review by the operator of a segment of 50-75mm of the coronary vessel at steps of 0.5-1mm accounting for 75-100 images in each OCT run, which is time consuming and necessitates some expertise in OCT analysis.In this paper we developed a new deep learning algorithm to assist the physician to identify and quantify coronary calcifications promptly, efficiently and accurately. Our algorithm achieves an accuracy of 0.9903 ± 0.009 over the test set at size of 1500 frames and even managed to find calcifications that weren’t recognized manually by the physician. For the best knowledge of the authors our algorithm achieves high accuracy which was never achieved in the past.

scholarly journals Histopathology-Based Deep-Learning Predicts Atherosclerotic Lesions in Intravascular Imaging

2021 ◽  
Vol 8 ◽  
Author(s):  
Olle Holmberg ◽  
Tobias Lenz ◽  
Valentin Koch ◽  
Aseel Alyagoob ◽  
Léa Utsch ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Expert Knowledge ◽  
Learning Algorithm ◽  
Atherosclerotic Lesion ◽  
Prediction Algorithm ◽  
Optical Coherence ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Atherosclerotic Lesions

Background: Optical coherence tomography is a powerful modality to assess atherosclerotic lesions, but detecting lesions in high-resolution OCT is challenging and requires expert knowledge. Deep-learning algorithms can be used to automatically identify atherosclerotic lesions, facilitating identification of patients at risk. We trained a deep-learning algorithm (DeepAD) with co-registered, annotated histopathology to predict atherosclerotic lesions in optical coherence tomography (OCT).Methods: Two datasets were used for training DeepAD: (i) a histopathology data set from 7 autopsy cases with 62 OCT frames and co-registered histopathology for high quality manual annotation and (ii) a clinical data set from 51 patients with 222 OCT frames in which manual annotations were based on clinical expertise only. A U-net based deep convolutional neural network (CNN) ensemble was employed as an atherosclerotic lesion prediction algorithm. Results were analyzed using intersection over union (IOU) for segmentation.Results: DeepAD showed good performance regarding the prediction of atherosclerotic lesions, with a median IOU of 0.68 ± 0.18 for segmentation of atherosclerotic lesions. Detection of calcified lesions yielded an IOU = 0.34. When training the algorithm without histopathology-based annotations, a performance drop of >0.25 IOU was observed. The practical application of DeepAD was evaluated retrospectively in a clinical cohort (n = 11 cases), showing high sensitivity as well as specificity and similar performance when compared to manual expert analysis.Conclusion: Automated detection of atherosclerotic lesions in OCT is improved using a histopathology-based deep-learning algorithm, allowing accurate detection in the clinical setting. An automated decision-support tool based on DeepAD could help in risk prediction and guide interventional treatment decisions.

scholarly journals Identification of coronary calcifications in optical coherence tomography imaging using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yarden Avital ◽  
Akiva Madar ◽  
Shlomi Arnon ◽  
Edward Koifman
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Image Acquisition ◽  
Percutaneous Treatment ◽  
Optical Coherence ◽  
Optimal Method ◽  
Deep Learning Algorithm ◽  
Coronary Calcifications ◽  
Artery Disease

AbstractCoronary calcifications are an obstacle for successful percutaneous treatment of coronary artery disease patients. The optimal method for delineating calcifications extent is coronary optical coherence tomography (OCT). To identify calcification on OCT and subsequently tailor the appropriate treatment, requires expertise in both image acquisition and interpretation. Image acquisition consists from system calibration, blood clearance by a contrast agent along with synchronization of the pullback process. Accurate interpretation demands careful review by the operator of a segment of 50–75 mm of the coronary vessel at steps of 5–10 frames per mm accounting for 375–540 images in each OCT run, which is time consuming and necessitates some expertise in OCT analysis. In this paper we developed a new deep learning algorithm to assist the physician to identify and quantify coronary calcifications promptly, efficiently and accurately. Our algorithm achieves an accuracy of 0.9903 ± 0.009 over the test set at size of 1500 frames and even managed to find calcifications that were not recognized manually by the physician. For the best knowledge of the authors our algorithm achieves high accuracy which was never achieved in the past.

scholarly journals Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Reza Mirshahi ◽  
Pasha Anvari ◽  
Hamid Riazi-Esfahani ◽  
Mahsa Sardarinia ◽  
Masood Naseripour ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Healthy Subjects ◽  
Optical Coherence Tomography Angiography ◽  
Validation Dataset ◽  
Diabetic Patients ◽  
Dice Similarity Coefficient ◽  
Optical Coherence ◽  
Avascular Zone

AbstractThe purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device’s built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device’s built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland–Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of − 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of − 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of − 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device’s built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

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