Dataset Information

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

ABSTRACT:

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. The stage was mounted on a surgical microscope, which enabled safe, stable positioning. Image processing steps included image alignment, noise reduction, and calibration. 3D visualization included alignment of the optic axis, multiplanar reformatting at arbitrary orientations, and volume rendering with optimized transfer functions. Scans were performed on cadaver and rabbit eyes.

Results

3D-UBM allowed visualization of the anterior segment tissues within a 3D anatomical context, unlike 2D-UBM. En face views and interactive slicer operations suggested an ability to plan and assess treatments, including lens placement and microcatheter cannulation of Schlemm's canal. Interactive software allowed us to make accurate measurements of tissue structures (e.g., iridocorneal angles, cyst volumes). In addition, unique measurements of ciliary tissues included single ciliary process volumes of 0.234 ± 0.093 mm3 with surface areas of 3.02 ± 1.07 mm2 and ciliary muscle volume of 67.87 mm3.

Conclusions

3D-UBM imaging of the anterior segment can be used to enable unique visualization and quantification of anterior segment structures.

Translational relevance

3D-UBM provides informative 3D imaging of tissues in the eye that are invisible to light to potentially provide physicians with improved diagnosis, treatment planning, and treatment assessment as compared to conventional 2D-UBM.

SUBMITTER: Helms RW

PROVIDER: S-EPMC7961115 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeThis study aimed to develop a fully automated deep learning ciliary body segmentation and assessment approach in three-dimensional ultrasound biomicroscopy (3D-UBM) images.MethodsEach 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.ResultsWe trained and tested the models on 4320 radial and 3864 en face images from 12 cadaver eye volumes. Deep learning models trained on radial images with Dice loss achieved the highest mean F1-score (0.89) for ciliary body segmentation. For three-class segmentation (ciliary muscle, processes, and background), radial images with Dice loss achieved the highest mean F1-score (0.75 for the ciliary process and 0.82 for the ciliary muscle). Part of the ciliary muscle (10.9%) was misclassified as the ciliary process and vice versa, which occurred owing to the difficulty in differentiating the ciliary muscle-processes border, even by experts. Deep learning segmentation made further editing by experts at least seven times faster than a fully manual approach. In eight cadaver eyes, the average ciliary muscle, process, and body volumes were 56 ± 9, 43 ± 13, and 99 ± 18 mm3, respectively. The average surface area of the ciliary muscle, process, and body were 346 ± 45, 363 ± 83, and 709 ± 80 mm2, respectively. We performed transscleral cyclophotocoagulation in cadaver eyes to shrink the ciliary processes. Both manual and automated measurements from deep learning segmentation show a decrease in volume, surface area, and 360° cross-sectional area measurements.ConclusionsThe proposed deep learning segmentation of the ciliary body and 3D measurements showed transscleral cyclophotocoagulation-related changes in the ciliary body.Translational relevanceAutomated ciliary body assessment using 3D-UBM has the translational potential for ophthalmic treatment planning and monitoring.