Visualization of Cataract Surgery Steps With 4D Microscope-Integrated Swept-Source Optical Coherence Tomography in Ex Vivo Porcine Eyes.

Purpose: To investigate the visualization capabilities of high-speed swept-source optical coherence tomography (SS-OCT) in cataract surgery. Methods: Cataract surgery was simulated in wet labs with ex vivo porcine eyes. Each phase of the surgery was visualized with a novel surgical microscope-integrated SS-OCT with a variable imaging speed of over 1 million A-scans per second. It was designed to provide four-dimensional (4D) live-volumetric videos, live B-scans, and volume capture scans. Results: Four-dimensional videos, B-scans, and volume capture scans of corneal incision, ophthalmic viscosurgical device injection, capsulorrhexis, phacoemulsification, intraocular lens (IOL) injection, and position of unfolded IOL in the capsular bag were recorded. The flexibility of the SS-OCT system allowed us to tailor the scanning parameters to meet the specific demands of dynamic surgical steps and static pauses. The entire length of the eye was recorded in a single scan, and unfolding of the IOL was visualized dynamically. Conclusions: The presented novel visualization method for fast ophthalmic surgical microscope-integrated intraoperative OCT imaging in cataract surgery allowed the visualization of all major steps of the procedure by achieving large imaging depths covering the entire eye and high acquisition speeds enabling live volumetric 4D-OCT imaging. This promising technology may become an integral part of routine and advanced robotic-assisted cataract surgery in the future. Translational Relevance: We demonstrate the visualization capabilities of a cutting edge swept-source OCT system integrated into an ophthalmic surgical microscope during cataract surgery.

Multiple instance learning based classification of diabetic retinopathy in weakly-labeled widefield OCTA en face images.

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.

Live 4D-OCT denoising with self-supervised deep learning.

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.