SimIntestine:来自虚拟囊内镜的合成数据集
Sarita Singh1, Basabi Bhaumik1, Shouri Chatterjee1
1Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India.
Medical image analysis
|July 5, 2025
概括
研究人员创建了SimIntestine,这是人类胃肠道的现实虚拟数据集,具有位置注释图像和深度图. 这一数据集有助于深度学习,用于内镜姿势和深度估计,克服现实数据的局限性.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 胃肠病学 胃肠病学
背景情况:
- 准确的位置注释数据集对于胃肠道内镜中的深度学习模型至关重要.
- 现有的合成数据集缺乏消化道的真实解剖和纹理特征.
- 从真实内镜获得注释数据以估计姿势和深度存在挑战.
研究的目的:
- 开发一种方法来生成一个现实的,位置注释的人体肠道的合成数据集.
- 为了提供地面真相,深度地图和摄像头姿势信息用于训练和评估深度学习模型.
- 为了解决目前用于内镜视频分析的数据集的局限性.
主要方法:
- 产生具有现实的解剖特征 (例如,plicae circulares,villi,haustral folds) 和纹理的小肠和大肠的虚拟模型.
- 集成一个虚拟囊内镜模拟现实世界的导航和图像捕捉.
- 在虚拟环境中纳入生理过程,如围心静止.
- 生成位置注释图像,地面真实深度地图以及相机定向/位置数据.
主要成果:
- 该SimIntestine数据集提供了人体消化道的物理现实的,注释的合成数据.
- 数据集包括独特的解剖特征,并模拟生理过程.
- 使用SimIntestine评估的基准技术 (Endo-SfMLearner,Monodepth2),通过增强的绩效指标证明其有效性.
结论:
- "SimIntestine"作为一个有价值的基准来改善内镜视频分析,特别是在姿势估计和同时定位和映射方面.
- 生成的数据集克服了现实世界无注释数据的局限性.
- 定量评估证实了数据集的有效性及其提高模型性能的能力.
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