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Anatomy of the Eyeball

Anatomy of the Eyeball

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...

Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

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Updated: Jul 26, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

利用多粒度视觉特征来对人类眼睛的视网膜层进行细分.

Xiang He^1,2, Yiming Wang³, Fabio Poiesi³

¹School of Mechanical Engineering, Shandong University, Jinan, China.

Frontiers in bioengineering and biotechnology

|June 16, 2023

概括

此摘要是机器生成的。

准确的视网膜层细分有助于早期发现眼睛疾病. 新的ConvNeXt网络采用了全新的注意力模块和多尺度结构,实现了最先进的结果,在新数据集上表现优于现有方法.

关键词:

接下来我们来谈谈一下.NR206 NR206 是一个非常重要的数字.深度学习是一种深度学习.多级别层分段化多层分段化光学连贯性断层扫描技术

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Using Retinal Imaging to Study Dementia

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Efficient and Consistent Generation of Retinal Pigment Epithelium/Choroid Flatmounts from Human Eyes for Histological Analysis

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Using Retinal Imaging to Study Dementia

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科学领域:

医学成像医学成像
计算机视觉计算机视觉计算机视觉
眼科医生眼科眼科

背景情况:

准确的视网膜层细分对于早期的眼科疾病检测至关重要.
现有的细分算法往往缺乏多细分特征利用和数据集可用性.
深度学习解决方案需要可访问的,高质量的数据集,以进行强大的模型培训.

研究的目的:

开发一个先进的端到端深度学习网络,用于精确的视网膜层细分.
为视网膜图像细分研究引入一个新的数据集 (NR206).
在细分模型中增强特征保留和多尺度分析.

主要方法:

提出了一种使用ConvNeXt架构的全新的端到端视网膜层细分网络.
整合了一种深度高效的注意模块和多尺度结构,以保存特征地图细节.
开发并发布了NR206数据集,包括206个健康的人类视网膜图像用于语义细分.

主要成果:

拟议的网络在NR206数据集上取得了卓越的表现,平均Dice得分为91.3%,mIoU为84.4%.
该模型展示了对玻璃眼和糖尿病黄斑 (DME) 数据集的最新性能.
NR206数据集是用户友好的,不需要额外的转码.

结论:

基于ConvNeXt的新型网络在视网膜层细分精度方面取得了显著的进步.
公共可用的NR206数据集和源代码将促进眼科深度学习的进一步研究.
该模型在多个眼科数据集中的有效性突显了其多功能性和潜在的临床适用性.