Deep learning segmentation of ciliary tissues using 3D ultrasound biomicroscopy (3D-UBM) images

Ahmed Tahseen Minhaz; Duriye Damla Sevgi; Sunwoo Kwak; Alvin Kim; Talia Burstein; Nithya Kanagasegar; Hao Wu; Richard Helms; Mahdi Bayat; Faruk Orge; David L. Wilson

doi:10.1117/12.2613025

4 April 2022 Deep learning segmentation of ciliary tissues using 3D ultrasound biomicroscopy (3D-UBM) images

Ahmed Tahseen Minhaz, Duriye Damla Sevgi, Sunwoo Kwak, Alvin Kim, Talia Burstein, Nithya Kanagasegar, Hao Wu, Richard Helms, Mahdi Bayat, Faruk Orge, David L. Wilson

Author Affiliations +

Proceedings Volume 12038, Medical Imaging 2022: Ultrasonic Imaging and Tomography; 120380V (2022) https://doi.org/10.1117/12.2613025
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

We developed a 3D ultrasound biomicroscopy (3D-UBM) imaging system and used it to assess ciliary tissues in the eye. As ultrasound can penetrate opaque ocular tissues, 3D-UBM has a unique ability to creating informative 3D visualization of anterior ocular structures not visible with other, optical imaging modalities. Ciliary body, located behind the iris, is responsible for fluid production making it an important ocular structure for glaucoma. Only 3DUBM allows visualization and measurements of ciliary body. Several steps were required for visualization and quantitative assessment. To reduce eye motion in 3D-UBM volumes, we performed slice alignment using Transformation Diffusion approach to avoid geometric artifacts. We applied noise reduction and aligned the volumes to the optic axis to create 3D renderings of ciliary body in its entirety. We extracted two different sets of images from these volumes, namely en face and radial images. We created a dataset of eye volumes with slices containing ciliary body, segmented by two analyst trainees and approved by two experts. Deep learning segmentation models (UNet and Inception-v3+) were trained on both sets of images using appropriate loss functions. Using en face images and Inception-v3+, and weighted cross entropy loss, we obtained Dice = 0.81±0.04. Using radial images, Inception-v3+, and with Dice loss, results were improved to Dice = 0.89±0.03, probably because radial images enable full usage of the symmetry of the eye. Cyclophotocoagulation (CPC) is a glaucoma treatment that is used to destroy the ciliary body partially or completely and reduce fluid production. 3D-UBM allows one to visualize and quantitatively analyze CPC treatments.

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Ahmed Tahseen Minhaz, Duriye Damla Sevgi, Sunwoo Kwak, Alvin Kim, Talia Burstein, Nithya Kanagasegar, Hao Wu, Richard Helms, Mahdi Bayat, Faruk Orge, and David L. Wilson "Deep learning segmentation of ciliary tissues using 3D ultrasound biomicroscopy (3D-UBM) images", Proc. SPIE 12038, Medical Imaging 2022: Ultrasonic Imaging and Tomography, 120380V (4 April 2022); https://doi.org/10.1117/12.2613025

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