Deep Learning Segmentation, Visualization, and Automated 3D Assessment of Ciliary Body in 3D Ultrasound Biomicroscopy Images.

Purpose: This study aimed to develop a fully automated deep learning ciliary body segmentation and assessment approach in three-dimensional ultrasound biomicroscopy (3D-UBM) images.

Methods: Each 3D-UBM eye volume was aligned to the optic axis via multiplanar reformatting. Ciliary muscle and processes were manually annotated, and Deeplab-v3+ models with different loss functions were trained to segment the ciliary body (ciliary muscle and processes) in both en face and radial images.

Clinical 3D Imaging of the Anterior Segment With Ultrasound Biomicroscopy.

Purpose: Ultrasound biomicroscopy (UBM) is an important ophthalmic imaging modality due to its ability to see behind pigmented iris and to visualize anterior chamber when the eye's transparency is compromised. We created a three-dimensional UBM (3D-UBM) system and acquired example images to illustrate its potential.

Methods: A commercial 50-MHz two-dimensional UBM (2D-UBM) system was attached to a precision translation stage and translated across the eye to acquire an image volume.

Keratoprosthesis Decentration and Tilt Results in Degradation in Image Quality.

Purpose: To study the effect of decentration and tilt of the type I Boston keratoprosthesis (KPro) on image quality in both aphakic and pseudophakic eyes.

Methods: An optical ray-tracing program was used to simulate the image projected onto the retina in an eye with a perfectly centered KPro, and in eyes with varying degrees of KPro decentration and tilt. Decentration was modeled along a typical white-to-white distance of 12.