The endoscope is a popular imaging modality used in many preevaluations and surgical treatments, and is also one of the essential tools in minimally invasive surgery. However, regular endoscopes provide only 2-D images. Even though stereoendoscopy systems can display 3-D images, the real anatomical structure of the observed lesion is unavailable and can only be judged by the surgeon's imagination. In this paper, we present a constraint-based factorization method for reconstructing 3-D structures registered to the patient, from 2-D endoscopic images. The proposed method incorporates the geometric constraints from the tracked surgical instrument into the traditional factorization method based on frame-to-frame feature motion on the endoscopically viewed scene. Experiments with real and synthetic data demonstrate good real-scale 3-D extraction, with greater accuracy than is available from traditional methods. The reconstruction process can also be accomplished in a few seconds, making it suitable for on-line surgical applications to provide surgeons with additional 3-D shape information, critical distance monitoring and warnings.

译文

:内窥镜是在许多预评估和手术治疗中使用的流行成像方式,也是微创手术中必不可少的工具之一。但是,常规内窥镜仅提供2D图像。即使立体内窥镜系统可以显示3D图像,所观察到的病变的真实解剖结构也不可用,只能由外科医生的想象力来判断。在本文中,我们提出了一种基于约束的因式分解方法,用于从2-D内窥镜图像重建注册给患者的3-D结构。所提出的方法将来自被跟踪的手术器械的几何约束纳入基于内窥镜观察场景中帧到帧特征运动的传统分解方法中。使用真实数据和合成数据进行的实验表明,可以进行良好的真实3D提取,其准确性要高于传统方法。重建过程也可以在几秒钟内完成,使其适用于在线手术应用,从而为外科医生提供额外的3-D形状信息,关键距离监控和警告。

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