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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

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Updated: Jun 26, 2026

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StrabNet-CQ:一个集成的深度学习框架,用于使用眼界地标检测进行自动化斜视的分类和量化.

Shubh Garg1, Ashish Sunkarapalli2, Debabrata Ghosh1

  • 1Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India.

BMC ophthalmology
|October 23, 2025
PubMed
概括

深度学习框架StrabNet-CQ使用眼睛图像准确地检测和分类. 这种自动化系统提供了客观的量化,改进了传统方法.

关键词:
人工智能的人工智能是人工智能.分类 分类 分类 分类.深度学习是一种深度学习.里程碑检测检测地标的检测眼睛的偏差 眼睛的偏差眼睛的错位是眼睛的错位.这是一种眼 (Strabismus).斯特拉比斯莫斯的量化测量

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科学领域:

  • 眼科和计算机视觉眼科和计算机视觉
  • 医疗保健中的人工智能

背景情况:

  • 视,眼睛 misalignment,影响双眼视力,传统上使用手动镜二光透镜 (PD) 读数进行诊断.
  • 目前的诊断方法是主观的,容易发生临床间的变化,并提供粗略的偏差测量.

研究的目的:

  • 开发和评估StrabNet-CQ,这是一个深度学习框架,用于自动化斜视的分类和量化.
  • 提供一个客观和精确的替代传统的诊断技术.

主要方法:

  • 使用深度学习框架 (StrabNet-CQ) 分析了600张眼睛图像.
  • 使用YOLOv8进行初始分类 (正常/异常,特定类型) 和ResNet101对细分眼部区域进行精细分类.
  • 使用ResNet18用于眼睛地标检测,以计算偏差指数和角度偏差.

主要成果:

  • 在视检测方面达到94%的准确性,在分类方面达到90%.
  • 对于正常,异位和低位的高灵敏度被证明.
  • 衍生参数与手动的PD值相关性很好 (r=0.733),使量化成为可能.

结论:

  • 斯特拉布网-CQ提供客观的斜视诊断和量化.
  • 该框架显示了临床部署的潜力,提高了对的检测和测量.