深度学习在医学图像配准上的研究进展与挑战

Research progress and challenges of deep learning in medical image registration

随着影像引导手术和放射治疗的发展,临床对医学图像配准研究的需求更强烈,带来的挑战也更大。最近几年,深度学习,特别是深度卷积神经网络,在医学图像处理方面取得了优异的成绩,在医学图像配准上的研究发展迅速。本文按技术方法分类总结了基于深度学习的医学图像配准的国内外研究进展,包括了基于优化策略的相似性估计、直接估计医学图像配准的变换参数等。然后分析了深度学习方法在医学图像配准上的挑战,并提出了可能的解决办法和研究方向。

With the development of image-guided surgery and radiotherapy, the demand for medical image registration is stronger and the challenge is greater. In recent years, deep learning, especially deep convolution neural networks, has made excellent achievements in medical image processing, and its research in registration has developed rapidly. In this paper, the research progress of medical image registration based on deep learning at home and abroad is reviewed according to the category of technical methods, which include similarity measurement with an iterative optimization strategy, direct estimation of transform parameters, etc. Then, the challenge of deep learning in medical image registration is analyzed, and the possible solutions and open research are proposed.