Ye, Run Zhou

Purpose: To describe an improved snare technique for the removal of an intraocular foreign body (IOFB) from the posterior segment. Methods: The snare is fashioned from a 23 or 25-gauge blunt-tipped cannula, a 5-0 or 6-0 nylon suture, and a cotton-tipped applicator. The snare is introduced through a 23 or 25-gauge vitrectomy port and tightened around the IOFB, maintaining a closed system. The IOFB can be extracted from the eye by pulling the cannula and extending the circumferential sclerotomy. Results: Four cases of IOFBs were treated using the improved nylon suture snare technique. Three of the four were traumatic open-globe injuries with retained IOFB, for which the snare technique was suitable for removal of the object. In the fourth case, the snare technique was used to remove a posteriorly dislocated silicone intraocular lens optic. No cases of postoperative retinal detachments or endophthalmitis were observed. Conclusion: This snare is fashioned from readily available, inexpensive materials and provides a safe and reliable method for IOFB removal through the vitrectomy port. By maintaining a closed system during IOFB mobilization and removal, the view through the cornea remains stable and intraocular pressure remains constant. Moreover, the snare can grasp irregularly shaped or large IOFBs.

ABSTRACT Purpose Optical coherence tomography (OCT) is a widely used imaging modality in ophthalmology. Accurate semantic segmentation of these images is critical for both clinical and research applications, yet existing convolutional neural network (CNN)-based methods face challenges in generalizability and robustness. This study introduces MAYOCTransformer, the first transformer-based deep learning model for comprehensive semantic segmentation of OCT images, and evaluates its performance against CNN-based models. Methods A large dataset of 3,500 OCT images was manually segmented using an iterative deep learning-assisted workflow. The MAYOCTransformer model, based on the Mask2Former architecture, was trained and compared against CNN-based segmentation models, including U-Net, U-Net++, FPN, and DeepLabV3+. Comprehensive segmentation tasks included 10 retinal layer segmentation, choroid stroma and vessel segmentation, and the identification of 9 types of discrete pathological findings including intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED), subretinal hyper-reflective material (SHRM), intraretinal hyper-reflective foci, and reticular pseudodrusen. Model performance was evaluated using the Dice similarity coefficient (DSC) on a hold-out test set with five-fold cross-validation. Additional validation was performed using external datasets, open-source segmentation models, and a randomized blinded expert evaluation. Results MAYOCTransformer outperformed CNN-based models in most segmentation tasks. Choroid segmentation performance was comparable between MAYOCTransformer and CNN models. External validation demonstrated the model’s generalizability, achieving higher DSC scores than publicly available segmentation models. A blinded expert evaluation showed that MAYOCTransformer’s segmentation was non-inferior to manual annotations. Conclusion MAYOCTransformer provides improved segmentation performance over CNN-based models. Its ability to generalize to external datasets suggests potential applicability in clinical and research settings.

Purpose: Augmented reality (AR) may allow vitreoretinal surgeons to leverage microscope-integrated digital imaging systems to analyze and highlight key retinal anatomic features in real time, possibly improving safety and precision during surgery. By employing convolutional neural networks (CNNs) for retina vessel segmentation, a retinal coordinate system can be created that allows pre-operative images of capillary non-perfusion or retinal breaks to be digitally aligned and overlayed upon the surgical field in real time. Such technology may be useful in assuring thorough laser treatment of capillary non-perfusion or in using pre-operative optical coherence tomography (OCT) to guide macular surgery when microscope-integrated OCT (MIOCT) is not available. Methods: This study is a retrospective analysis involving the development and testing of a novel image-registration algorithm for vitreoretinal surgery. Fifteen anonymized cases of pars plana vitrectomy with epiretinal membrane peeling, along with corresponding preoperative fundus photographs and optical coherence tomography (OCT) images, were retrospectively collected from the Mayo Clinic database. We developed a TPU (Tensor-Processing Unit)-accelerated CNN for semantic segmentation of retinal vessels from fundus photographs and subsequent real-time image registration in surgical video streams. An iterative patch-wise cross-correlation (IPCC) algorithm was developed for image registration, with a focus on optimizing processing speeds and maintaining high spatial accuracy. The primary outcomes measured were processing speed in frames per second (FPS) and the spatial accuracy of image registration, quantified by the Dice coefficient between registered and manually aligned images. Results: When deployed on an Edge TPU, the CNN model combined with our image-registration algorithm processed video streams at a rate of 14 FPS, which is superior to processing rates achieved on other standard hardware configurations. The IPCC algorithm efficiently aligned pre-operative and intraoperative images, showing high accuracy in comparison to manual registration. Conclusions: This study demonstrates the feasibility of using TPU-accelerated CNNs for enhanced AR in vitreoretinal surgery.

Objective: The application of computer models in continuous patient activity monitoring using video cameras is complicated by the capture of images of varying qualities due to poor lighting conditions and lower image resolutions. Insufficient literature has assessed the effects of image resolution, color depth, noise level, and low light on the inference of eye opening and closing and body landmarks from digital images. Method: This study systematically assessed the effects of varying image resolutions (from 100 × 100 pixels to 20 × 20 pixels at an interval of 10 pixels), lighting conditions (from 42 to 2 lux with an interval of 2 lux), color-depths (from 16.7 M colors to 8 M, 1 M, 512 K, 216 K, 64 K, 8 K, 1 K, 729, 512, 343, 216, 125, 64, 27, and 8 colors), and noise levels on the accuracy and model performance in eye dimension estimation and body keypoint localization using the Dlib library and OpenPose with images from the Closed Eyes in the Wild and the COCO datasets, as well as photographs of the face captured at different light intensities. Results: The model accuracy and rate of model failure remained acceptable at an image resolution of 60 × 60 pixels, a color depth of 343 colors, a light intensity of 14 lux, and a Gaussian noise level of 4% (i.e., 4% of pixels replaced by Gaussian noise). Conclusions: The Dlib and OpenPose models failed to detect eye dimensions and body keypoints only at low image resolutions, lighting conditions, and color depths. Clinical Impact: Our established baseline threshold values will be useful for future work in the application of computer vision in continuous patient monitoring.