We propose a novel image registration method based on implicit neural representations that addresses the challenging problem of registering a pair of brain images with similar anatomical structures, but where one image contains additional features or artifacts that are not present in the other image. To demonstrate its effectiveness, we use 2D microscopy $\textit{in situ}$ hybridization gene expression images of the marmoset brain. Accurately quantifying gene expression requires image registration to a brain template, which is difficult due to the diversity of patterns causing variations in visible anatomical brain structures. Our approach uses implicit networks in combination with an image exclusion loss to jointly perform the registration and decompose the image into a support and residual image. The support image aligns well with the template, while the residual image captures individual image characteristics that diverge from the template. In experiments, our method provided excellent results and outperformed other registration techniques.

翻译：我们提出了一种基于隐式神经表征的新型图像配准方法，用于解决具有相似解剖结构但其中一幅图像包含另一图像中不存在额外特征或伪影的脑图像配准难题。为验证其有效性，我们采用狨猴脑的二维显微镜原位杂交基因表达图像。精确量化基因表达需要将图像配准到脑模板，但由于基因表达模式的多样性导致可见解剖结构变异，这一过程极具挑战性。该方法通过结合隐式网络与图像排除损失，联合执行配准运算并将图像分解为支撑图像与残差图像。支撑图像与模板保持良好对齐，而残差图像则捕捉偏离模板的个体图像特征。实验结果表明，该方法取得了优异效果，性能优于其他配准技术。