• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computer methods in biomechanics and biomedical engineering >Synthetic laparoscopic video generation for machine learning-based surgical instrument segmentation from real laparoscopic video and virtual surgical instruments
【24h】

Synthetic laparoscopic video generation for machine learning-based surgical instrument segmentation from real laparoscopic video and virtual surgical instruments

机译：基于机器学习的手术仪器分割的合成腹腔镜视频和虚拟外科仪器

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper proposes a synthetic laparoscopic image generation for machine-learning-based surgical instrument segmentation from laparoscopic videos. Recently surgical instrument extraction methods from laparoscopic videos have been studied using deep learning, fuelling the creation of a large amount of training data for better performance. However, it is difficult to collect a massive amount of data on surgical instruments that are used infrequently during surgery. Their recognition accuracy may be reduced by the lack of training data. This paper solves this problem by increasing the training data with an image synthesis technique. Pairs of synthetic laparoscopic videos and their labelled data are automatically generated by superimposing 3D virtual surgical instrument models on real laparoscopic videos. The synthetic laparoscopic images are translated using CycleGAN so that the appearance of the surgical instruments closely resembles those in the real laparoscopic videos. Additionally, we extracted surgical instruments from laparoscopic videos using 2D U-Net based network. This network was trained using both the synthetic laparoscopic images and the manually labelled, real laparoscopic video data. The trained model extracted the surgical instruments from the laparoscopic videos. Our experimental result showed that the recognition accuracy of the surgical instruments, which are used infrequently during surgery, was improved using synthetic laparoscopic images generated by our proposed method.
机译：本文提出了腹腔镜视频的基于机器学习的外科手术仪器分割的合成腹腔镜图像。最近，使用深度学习研究了来自腹腔镜视频的外科仪器提取方法，推动了创建大量培训数据以获得更好的性能。然而，难以收集在手术期间不经常使用的外科手术器械的大量数据。通过缺乏培训数据，可以减少他们的认可准确性。本文通过使用图像合成技术增加训练数据来解决这个问题。通过在真正的腹腔镜视频上叠加3D虚拟手术器械模型，自动生成一对合成腹腔镜视频及其标记数据。合成腹腔镜图像使用CycleanGan翻译，使外科手术器械的外观非常类似于真实腹腔镜视频。此外，我们使用基于2D U-Net网络从腹腔镜视频中提取了从腹腔镜视频的手术器械。使用合成腹腔镜图像和手动标记的实际腹腔镜视频数据进行培训此网络。训练有素的模型从腹腔镜视频中提取了手术器械。我们的实验结果表明，使用我们所提出的方法产生的合成腹腔镜图像，改善了手术中不经常使用的外科手术器械的识别准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号