解决共聚焦与染色切片图像自动配准难题，提升病理分析效率。
Automatic Registration of SHG and H&E Images with Feature-based Initial Alignment and Intensity-based Instance Optimization: Contribution to the COMULIS Challenge
- 基于特征点匹配与实例优化的可变形配准方法
- 初始配准成功率达88%，目标配准误差均值2.48
- 无需训练，适用于多模态病理图像融合
非侵入式二次谐波显微镜（SHG）与苏木精-伊红染色（H&E）切片的自动配准是一个迫切需求但尚未解决的问题。由于SHG图像仅包含部分信息，而H&E图像提供更丰富的组织形态信息，且两种成像方式的强度分布不同，该任务被建模为具有缺失数据的多模态配准问题。本文提出一种基于自动关键点匹配、随后进行基于实例优化的可变形配准的方法，无需训练，使用COMULIS组织提供的Learn2Reg挑战数据集进行评估。在外部验证集上，该方法展现出良好泛化能力，初始配准成功率达88%，平均目标配准误差为2.48。源代码已公开，并集成至DeeperHistReg图像配准框架中。
原文摘要 · Abstract (English)
The automatic registration of noninvasive second-harmonic generation microscopy to hematoxylin and eosin slides is a highly desired, yet still unsolved problem. The task is challenging because the second-harmonic images contain only partial information, in contrast to the stained H&E slides that provide more information about the tissue morphology. Moreover, both imaging methods have different intensity distributions. Therefore, the task can be formulated as a multi-modal registration problem with missing data. In this work, we propose a method based on automatic keypoint matching followed by deformable registration based on instance optimization. The method does not require any training and is evaluated using the dataset provided in the Learn2Reg challenge by the COMULIS organization. The method achieved relatively good generalizability resulting in 88% of success rate in the initial alignment and average target registration error equal to 2.48 on the external validation set. We openly release the source code and incorporate it in the DeeperHistReg image registration framework.
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