用合成数据增强测试时间解决了光谱成像中的分布变化
Ahmad Bin Qasim1,2,3, Alessandro Motta4, Alexander Studier-Fischer5
1Division of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany. ahmad.qasim@dkfz-heidelberg.de.
概括
由于数据有限和输液诱导的光谱变化,用于手术细分的高光谱成像 (HSI) 面临着挑战. 使用合成数据进行测试时间增长的新方法显著提高了细分性能,超过了当前的方法.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 手术技术 手术技术
背景情况:
- 手术场景细分对于具有上下文意识的外科辅助至关重要.
- 超光谱成像 (HSI) 与RGB数据相比,为细分提供了优势.
- 现有的HSI数据集有限,缺乏临床组织变异.
研究的目的:
- 探索由器官 perfusion 改变引起的光谱成像分布变化.
- 通过合成数据引入一种新的策略,以减轻这些分布转移.
主要方法:
- 分析了来自16只不同器官 perfusion 状态的猪的615张高光谱图像.
- 使用合成数据开发测试时间增长策略.
- 在诱导 perfusion 变化下对最先进的细分网络的评估.
主要成果:
- perfusion 变化显著影响了细分网络的性能,在缺血性脏中,脏下降了多达 93%.
- 拟议的方法比最先进的方法提高了4.6倍.
- 由于输液改变,观察到器官特异性性能下降的变化.
结论:
- 开发的方法提高了在光谱成像中神经网络的概括性.
- 这种方法显示了改善不同病理的外科手术协助的潜力.
- 解决分布转移对于在外科手术中强大的高光谱成像应用至关重要.
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