Reiter, Gregor S.

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.