Laparoscopic videos have been increasingly acquired for various purposes including surgical training and quality assurance, due to the wide adoption of laparoscopy in minimally invasive surgeries. However, it is very time consuming to view a large amount of laparoscopic videos, which prevents the values of laparoscopic video archives from being well exploited. In this paper, a dictionary selection based video summarization method is proposed to effectively extract keyframes for fast access of laparoscopic videos. Firstly, unlike the low-level feature used in most existing summarization methods, deep features are extracted from a convolutional neural network to effectively represent video frames. Secondly, based on such a deep representation, laparoscopic video summarization is formulated as a diverse and weighted dictionary selection model, in which image quality is taken into account to select high quality keyframes, and a diversity regularization term is added to reduce redundancy among the selected keyframes. Finally, an iterative algorithm with a rapid convergence rate is designed for model optimization, and the convergence of the proposed method is also analyzed. Experimental results on a recently released laparoscopic dataset demonstrate the clear superiority of the proposed methods. The proposed method can facilitate the access of key information in surgeries, training of junior clinicians, explanations to patients, and archive of case files.

中文翻译：

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通过多样化和加权字典选择从腹腔镜视频中提取关键帧

由于腹腔镜在微创手术中的广泛采用，腹腔镜视频越来越多地用于各种目的，包括手术培训和质量保证。然而，观看大量的腹腔镜视频非常耗时，这阻碍了腹腔镜视频档案的价值被很好地利用。在本文中，提出了一种基于字典选择的视频摘要方法，以有效地提取关键帧以快速访问腹腔镜视频。首先，与大多数现有摘要方法中使用的低级特征不同，深度特征是从卷积神经网络中提取的，以有效地表示视频帧。其次，基于这样的深度表示，腹腔镜视频摘要被制定为多样化和加权的字典选择模型，其中考虑图像质量来选择高质量的关键帧，并添加多样性正则化项以减少所选关键帧之间的冗余。最后，设计了一种收敛速度快的迭代算法进行模型优化，并对所提方法的收敛性进行了分析。最近发布的腹腔镜数据集的实验结果证明了所提出方法的明显优势。所提出的方法可以促进手术中关键信息的访问、初级临床医生的培训、对患者的解释以及病例档案的存档。针对模型优化设计了一种收敛速度快的迭代算法，并对所提方法的收敛性进行了分析。最近发布的腹腔镜数据集的实验结果证明了所提出方法的明显优势。所提出的方法可以促进手术中关键信息的访问、初级临床医生的培训、对患者的解释以及病例档案的存档。针对模型优化设计了一种收敛速度快的迭代算法，并对所提方法的收敛性进行了分析。最近发布的腹腔镜数据集的实验结果证明了所提出方法的明显优势。所提出的方法可以促进手术中关键信息的访问、初级临床医生的培训、对患者的解释以及病例档案的存档。