By providing three-dimensional visualization of tissues and instruments at high resolution, live volumetric optical coherence tomography (4D-OCT) has the potential to revolutionize ophthalmic surgery. However, the necessary imaging speed is accompanied by increased noise levels. A high data rate and the requirement for minimal latency impose major limitations for real-time noise reduction. In this work, we propose a low complexity neural network for denoising, directly incorporated into the image reconstruction pipeline of a microscope-integrated 4D-OCT prototype with an A-scan rate of 1.2 MHz. For this purpose, we trained a blind-spot network on unpaired OCT images using a self-supervised learning approach. With an optimized U-Net, only a few milliseconds of additional latency were introduced. Simultaneously, these architectural adaptations improved the numerical denoising performance compared to the basic setup, outperforming non-local filtering algorithms. Layers and edges of anatomical structures in B-scans were better preserved than with Gaussian filtering despite comparable processing time. By comparing scenes with and without denoising employed, we show that neural networks can be used to improve visual appearance of volumetric renderings in real time. Enhancing the rendering quality is an important step for the clinical acceptance and translation of 4D-OCT as an intra-surgical guidance tool.
Previously introduced deep learning classifiers were able to support diabetic biomarker detection in OCTA en face images, but require pixel-by-pixel expert labeling, which is a labor-intensive and expensive process. We present a multiple-instance learning-based network, MIL-ResNet,14 that detects clinically relevant diabetic retinopathy biomarkers in a wide-angle (65°) OCTA dataset with high accuracy without annotation. We evaluated our proposed architecture against two well-established machine learning classifiers, ResNet14 and VGG16. The dataset we used for this study was acquired with a MHz A-scan rate swept source OCT device. We used a total of 352 en face images representing the retinal vasculature over an 18 mm x 18 mm field of view. MIL-ResNet14 outperformed the other two networks with an F-score of 0.95, a precision of 0.909 and an area under the curve of 0.973. In addition, we were able to demonstrate that MIL-ResNet14 paid special attention to relevant biomarkers such as ischemic areas and retinal vascular abnormalities by saliency overlay of gradient-weighted class activation maps on top of the en face images. Thus, OCTA could be used as a powerful diagnostic decision support tool for clinical ophthalmic screening in combination with our MIL approach.
We present a multiple instance learning-based network, MIL-ResNet14, detecting biomarkers for diabetic retinopathy in a widefield optical coherence tomography angiography dataset with high accuracy, without the necessity of annotations other than the information of whether a scan stems from a diabetic patient or not. Previously introduced deep learning-based classifiers were able to support the detection of diabetic biomarkers in OCTA images, however, require expert labeling on a pixel-level, a labor-intensive and expensive process. We evaluated our proposed architecture against two proven-capable classifiers, ResNet14 and VGG16. The dataset we applied for this study was acquired with a MHz A-Scan rate widefield Swept Source-OCT device. We utilized a total of 352 en face images, displaying retinal vasculature over a field of view of 18 mm x 18 mm. MIL-ResNet14 outperformed both other networks with an F-score of 0.95, a precision of 0.909 and an Area Under the Curve of 0.973. In addition, we could show via saliency overlays of gradient-weighted class activation mappings onto the en face images, that MIL-ResNet14 pays special attention to clinically relevant biomarkers like ischemic areas and retinal vessel anomalies. This could therefore function as a vigorous diagnostic decision support tool for clinical ophthalmologic screenings.
Diabetic retinopathy (DR), a pathologic change of the human retinal vasculature, is the leading cause of blindness in working-age adults with diabetes mellitus. Optical coherence tomography angiography (OCTA), a functional extension of optical coherence tomography, has shown potential as a tool for early diagnosis of DR through its ability to visualize the retinal vasculature in all spatial dimensions. Previously introduced deep learning-based classifiers were able to support the detection of DR in OCTA images, but require expert labeling at the pixel level, a labor-intensive and expensive process. We present a multiple instance learning-based network, MIL-ResNet,14 that is capable of detecting biomarkers in an OCTA dataset with high accuracy, without the need for annotations other than the information whether a scan is from a diabetic patient or not. The dataset we used for this study was acquired with a diagnostic ultra-widefield swept-source OCT device with a MHz A-scan rate. We were able to show that our proposed method outperforms previous state-of-the-art networks for this classification task, ResNet14 and VGG16. In addition, our network pays special attention to clinically relevant biomarkers and is robust against adversarial attacks. Therefore, we believe that it could serve as a powerful diagnostic decision support tool for clinical ophthalmic screening.
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