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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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相关实验视频

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使用深度学习的全景放射图的风格协调.

Hak-Sun Kim1,2, Jaejung Seol3, Ji-Yun Lee1

  • 1Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea.

Oral radiology
|October 29, 2024
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概括

这项研究使用CycleGAN协调了不同牙科设备的全景放射图像. 人工智能模型成功地标准化了图像风格,提高了诊断用途的一致性.

关键词:
计算机 计算机 计算机深度学习是一种深度学习.神经网络的神经网络的神经网络这是一个全景景观.辐射图像增强技术 辐射图像增强技术放射学 放射学 放射学 放射学

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科学领域:

  • 牙科成像 牙科成像 牙科成像
  • 放射学中的人工智能
  • 图像处理 图像处理

背景情况:

  • 全景射线图对于牙科诊断至关重要.
  • 来自不同设备的图像风格的变化可能会影响解释.
  • 标准化图像外观对于一致的分析至关重要.

研究的目的:

  • 在一个机构内协调两种不同类型的设备 (雷斯扫描Alpha Plus和Pax-i plus) 的全景射线图像.
  • 为了在各种设备上实现一致的图像风格.
  • 评估CycleGAN在图像风格协调方面的有效性.

主要方法:

  • 使用CycleGAN协调7545个Pax-i加 (P单元) 图像以匹配8079个Rayscan Alpha Plus (R单元) 图像的风格.
  • 采用了客观指标:频率初始距离 (FID) 和学习感知图像补丁相似性 (LPIPS).
  • 两名口腔和大面部放射科医生对协调和原始图像进行了专家评估.

主要成果:

  • 与原始P单元图像 (8.380,0.519) 相比,转换的P单元图像显示出较低的FID (7.362) 和LPIPS (0.488).
  • 在协调后,观察到LPIPS显著减少 (p < 0.05).
  • 放射科医生发现43.3-46.7%的转换P单位图像与R单位风格相匹配.

结论:

  • CycleGAN展示了协调全景放射图像风格的潜力.
  • 人工智能模型有效地减少了不同设备之间的图像风格差异.
  • 为了更广泛的应用,建议使用额外的成像单元进行进一步的模型增强.