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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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伪SegRT:在外科手术期间用于OCT细分的有效伪标签.

Yu Huang1, Riaz Asaria2,3, Danail Stoyanov1,2

  • 1Department of Computer Science, University College London, London, UK.

International journal of computer assisted radiology and surgery
|May 26, 2023
PubMed
概括
此摘要是机器生成的。

一种新的半监督深度学习方法使用伪标签用于视网膜光学连贯断层扫描 (OCT) 细分,显著改善机器人眼科手术指导,使用最小的标签数据.

关键词:
深度学习, 伪标签实时的OCT细分实时的OCT细分机器人微手术是一种微手术.半监督学习 半监督学习

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科学领域:

  • 眼科的微手术是眼科的微手术.
  • 医学成像医学成像
  • 深度学习是一种深度学习.

背景情况:

  • 机器人眼科微手术为复杂的手术提供了更高的精度.
  • 术内光学相干断层扫描 (iOCT) 有助于可视化,但深度学习细分需要广泛的标记数据.

研究的目的:

  • 开发一种半监督方法用于视网膜OCT边界细分,以指导机器人手术.
  • 克服眼科手术深度学习中耗时的数据注释的局限性.

主要方法:

  • 一个基于U-Net的模型,利用一个伪标签策略,将标记和未标记的OCT扫描结合起来.
  • 用半监督方法训练模型,以提高细分精度.
  • 使用TensorRT优化和加速模型以实现实时性能.

主要成果:

  • 与完全监督的方法相比,伪标签方法在未见的数据上实现了更好的概括性和性能,仅使用2%的标签样本.
  • 加速的GPU推断实现了每1毫秒以下的速度,具有FP16精度.

结论:

  • 半监督的伪标签策略显示了在引导机器人手术系统中实时OCT细分的潜力.
  • 加速网络对OCT图像的精确细分和用于子视网膜注射的指导手术工具充满希望.