睫状体
超声生物显微镜
睫状肌
青光眼
眼科
睫状突
IRIS(生物传感器)
老花眼
医学
生物医学工程
解剖
计算机科学
人工智能
住宿
生物识别
物理
光学
作者
Ahmed Tahseen Minhaz,Hao Wu,Richard Helms,Duriye Damla Sevgi,Alvin Kim,Sunwoo Kwak,Faruk Örge,David L. Wilson
摘要
We developed a methodology for 3D assessment of ciliary body of the eye, an important, but understudied tissue, using our new 3D ultrasound biomicroscopy (3D-UBM) imaging system. The ciliary body produces aqueous humor, which if not drained properly, can lead to increased intraocular pressure and glaucoma, a leading cause of blindness. Most medications and some surgical procedures for glaucoma target the ciliary body. Ciliary body is also responsible for focusing-accommodation by muscle contraction and relaxation. UBM is the only imaging modality which can be used to visualize structures behind the opaque iris, such as ciliary body. Our 3D-UBM acquires several hundred high resolutions (50 MHz) 2D-UBM images and creates a 3D volume, enabling heretofore unavailable en face visualizations and quantifications. In this study, we calculated unique 3D biometrics from automated segmentation using deep learning (UNet). Our results show accuracy of 0.93 ± 0.01, sensitivity of 0.79 ± 0.07 and dice score of 0.72 ± 0.07 on deep learning segmentation of ciliary muscle. For an eye, volume of ciliary body was 67.87 mm3, single ciliary process volumes were 0.234 ± 0.093 mm3 with surface areas adjacent to aqueous humor of 3.02 ± 1.07 mm2. Automated and manual measurements of ciliary muscle volume and cross-sectional area are compared which show overestimation in volume measurement but higher agreeability in cross-sectional area measurements.
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