Eyeball Segmentation
Eyeball Segmentation - Related Videos
01:20
Anatomy of the Eyeball
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2024
The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle layer, the vascular tunic,...
07:01
Preparation and Analysis of Histological Slides of Rat and Mouse Eyeballs to Evaluate the Retina
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Cited by 1 •
2024
This manuscript presents a protocol for the histological preparation of slides from rodent eyeballs. With the proposed method, the inner retina can be easily visualized and assessed under a light microscope. The procedure is presented for the eyeballs of mice and rats.
04:48
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Cited by 4 •
2022
An object segmentation protocol for orbital computed tomography (CT) images is introduced. The methods of labeling the ground truth of orbital structures by using super-resolution, extracting the volume of interest from CT images, and modeling multi-label segmentation using 2D sequential U-Net for orbital CT images are explained for supervised learning.
09:42
A Chronic Autoimmune Dry Eye Rat Model with Increase in Effector Memory T Cells in Eyeball Tissue
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Cited by 10 •
2017
This report describes a method to induce chronic experimental autoimmune dry eye in Lewis rats through immunization with an emulsion of rat lacrimal gland extract, ovalbumin, and complete Freund's adjuvant, followed by the injection of lacrimal gland extract and ovalbumin into the forniceal subconjunctiva and lacrimal glands six weeks later.
11:38
Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench
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Cited by 8 •
2017
Segmentation of three-dimensional data from many imaging techniques is a major bottleneck in analysis of complex biological systems. Here, we describe the use of SuRVoS Workbench to semi-automatically segment volumetric data at various length-scales using example datasets from cryo-electron tomography, cryo soft X-ray tomography, and phase contrast X-ray tomography techniques.
FAQs
The process can combine intensity, color, texture, and shape information to locate anatomical boundaries. Intensity and color differences may separate regions with contrasting appearance, while texture helps distinguish tissues with different visual patterns. Shape provides structural constraints that support boundary detection when image values overlap. Combining these cues can produce a more reliable pixel-level mask than relying on one visual property alone.
These approaches identify boundaries through different mechanisms. Thresholding separates regions according to image values, while edge detection emphasizes transitions between neighboring areas. Deformable models adjust a boundary to fit anatomical contours, and machine-learning algorithms learn image patterns associated with target structures. The choice determines how the system uses appearance, boundary information, shape, or learned patterns to generate the mask.
Shape information helps constrain segmentation around anatomically meaningful boundaries, especially when adjacent tissues have similar intensity, color, or texture. It can guide the estimated contour toward a plausible ocular structure rather than treating every local image variation as a separate region. This improves the usefulness of the resulting mask for measurements, reconstruction, and computational modeling.
A typical workflow identifies the ocular region in a biomedical image, detects boundaries using suitable visual cues or algorithms, and produces a pixel-level mask for the eyeball or selected anatomical components. The mask can then be used for downstream analysis, such as quantifying structures, reconstructing three-dimensional geometry, or tracking changes between images. The exact algorithm depends on the image characteristics and engineering objective.
Engineers can apply the technique when a system must isolate ocular structures for quantitative or computational tasks. Supported uses include three-dimensional reconstruction, biometric identification, ophthalmic measurement, computer-assisted diagnosis, eye-tracking, surgical planning, and computational modeling. In each case, separating the relevant structures provides image regions that can be analyzed, compared, or incorporated into a larger technical system.
An accurate mask establishes the image locations and boundaries of the eyeball or its components, allowing researchers to quantify ocular structures and track their changes across images. It also supplies geometric information for reconstruction and modeling. In engineering applications, these measurements can support reliable eye-tracking systems, surgical planning, biometric identification, and computer-assisted diagnostic workflows.