单眼内镜图像深度估计与多尺度的残留融合图像
Shiyuan Liu1, Jingfan Fan2, Yun Yang3
1Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China; China Center for Information Industry Development, Beijing, 100081, China.
Computers in biology and medicine
|December 25, 2023
概括
这项研究引入了一种新的多尺度残留融合方法,用于精确的内镜手术中单眼深度估计. 该方法在具有挑战性的内镜图像中增强了深度感知,提高了手术精度.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 手术技术 手术技术
背景情况:
- 单眼深度估计对于临床内镜非常重要.
- 内镜图像带来了诸如连贯照明和缺乏纹理的表面等挑战.
- 现有的方法在这些环境中难以准确的深度感知.
研究的目的:
- 为单眼内镜深度估计提出一种新的多尺度残留融合方法.
- 为了克服内镜成像条件所带来的挑战.
- 为了提高深度估计的准确性,以改善外科手术指导.
主要方法:
- 使用图像频率域组件空间转换来稳定照明.
- 采用图像辐射强度减弱模型进行初始深度地图估计.
- 应用了多尺度的残留聚变优化技术来提炼.
主要成果:
- 在公共数据集上实现了高结构相似度 (0.94,0.82,0.84).
- 在不同的模型上,深度估计准确度为89.3%和91.2%.
- 在内镜图像中有效捕捉复杂的细节.
结论:
- 拟议的方法显示出临床内镜应用的巨大潜力.
- 促进可靠的深度估计,提高手术程序的质量.
- 在公共数据集上强大的性能验证了它的有效性.
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