产生光学相似的放射图,作为干预放射学深度学习的替代数据集
Wilbur K S Fum1,2, Mohammad Nazri Md Shah1, Raja Rizal Azman Raja Aman1
1Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
Physical and engineering sciences in medicine
|September 11, 2023
概括
从CT血管学数据生成合成光学图像,可以提高光学指导干预的深度学习模型性能. 这种方法提高了血管的可见性,为医疗成像中的有限标记数据提供了解决方案.
科学领域:
- 医疗成像医学成像
- 深度学习 (Deep Learning) 是一种深度学习.
- 放射学 放射学是一门学科.
背景情况:
- 在光学指导干预 (FGI) 中,获取用于深度学习 (DL) 的大型标记数据集具有挑战性.
- 对比CT数据的标准2D投影显示对比血管的可见性较差.
研究的目的:
- 开发一种替代方法,从对比的头部CT血管图 (CTA) 体积数据中生成光学类型的放射图.
- 提高合成数据的实用性,用于在FGI中的DL应用.
主要方法:
- 从CTA数据中对脑组织,骨和对比血管的细分.
- 应用算法来调整HU值以提高可见性.
- 通过标准基于射线的投影生成2D图像.
主要成果:
- 合成图像与临床光镜图像具有很好的感知相似性.
- 增强的合成和临床图像显示了统计学上相当的平均受试者对比度.
- 在使用合成图像进行解剖学地标定位等任务中,DL模型的性能得到了改善.
结论:
- 拟议的方法产生了类似于真实光镜图像的特征的合成图像.
- 合成图像为FGI中的DL提供了可行的替代数据源,解决了数据稀缺问题.
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