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Automatic GrabCut based lung extraction from endoscopic images with an initial boundary

机译：从具有初始边界的内窥镜图像中自动提取基于GrabCut的肺

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Endoscopic images provide doctors with valuable information in both diagnosis and surgery. In a thoracoscopic surgery, locating lung part based on endoscopic images is difficult since lungs vary sharply according to respiration. In this case, correctly extracting lung part plays a crucial role in intraoperative navigation. Although many efficient image segmentation approaches have been developed in the last two decades, they can rarely achieve reliable performance in lung segmentation due to the high deformability of lungs, similarity between lungs and background, unstable movement of the endoscope, and dynamic appearance changes of both lungs and background. In this research, we propose an effective approach for extracting lungs during endoscopic videos. The proposed algorithm is based on GrabCut which derives from max-flow min-cut theorem. However, unlike GrabCut that needs user interaction for each frame segmentation, the proposed method only requires an initial boundary of the first frame. Furthermore, it utilizes motion and boundary information to facilitate GrabCut to achieve a global optimum. The robustness of the proposed approach has been validated by experiments using clinical lung endoscopic videos.
机译：内窥镜图像为医生提供了诊断和手术中有价值的信息。在胸腔镜手术中，基于内窥镜图像的定位肺部是困难的，因为肺部根据呼吸急剧变化。在这种情况下，正确提取的肺部在术中导航中起着至关重要的作用。虽然在过去的二十年中已经开发了许多有效的图像分割方法，但由于肺部的肺部，肺部和背景之间的不稳定，内窥镜的不稳定运动，以及内窥镜的动态外观变化，它们很少在肺部分段中实现可靠的性能。肺和背景。在这项研究中，我们提出了一种有效的方法在内窥镜视频期间提取肺部。所提出的算法基于Grabcut，它来自MAX-Flow Min-Cut定理。然而，与需要用户交互的Grabcut不同，所提出的方法仅需要第一帧的初始边界。此外，它利用运动和边界信息来促进Grabcut实现全局最佳。通过使用临床肺内窥镜视频的实验验证了所提出的方法的稳健性。

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