The acquisition of high quality maps of gene expression in the rodent brain is of fundamental importance to the neuroscience community. The generation of such datasets relies on registering individual gene expression images to a reference volume, a task encumbered by the diversity of staining techniques employed, and by deformations and artifacts in the soft tissue. Recently, deep learning models have garnered particular interest as a viable alternative to traditional intensity-based algorithms for image registration. In this work, we propose a supervised learning model for general multimodal 2D registration tasks, trained with a perceptual similarity loss on a dataset labeled by a human expert and augmented by synthetic local deformations. We demonstrate the results of our approach on the Allen Mouse Brain Atlas (AMBA), comprising whole brain Nissl and gene expression stains. We show that our framework and design of the loss function result in accurate and smooth predictions. Our model is able to generalize to unseen gene expressions and coronal sections, outperforming traditional intensity-based approaches in aligning complex brain structures.

中文翻译：

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小鼠脑图谱多模态基因表达注册的具有感知相似性的监督学习

在啮齿动物大脑中获得高质量的基因表达图对神经科学界至关重要。此类数据集的生成依赖于将单个基因表达图像注册到参考体积，这项任务受到所采用的染色技术多样性以及软组织中的变形和伪影的阻碍。最近，作为传统的基于强度的图像配准算法的可行替代方案，深度学习模型引起了特别的兴趣。在这项工作中，我们提出了一种用于一般多模态 2D 注册任务的监督学习模型，在由人类专家标记并通过合成局部变形增强的数据集上使用感知相似性损失进行训练。我们在 Allen Mouse Brain Atlas (AMBA) 上展示了我们的方法的结果，包括全脑尼氏染色和基因表达染色。我们展示了我们的损失函数框架和设计可以实现准确和平滑的预测。我们的模型能够推广到看不见的基因表达和冠状切片，在对齐复杂的大脑结构方面优于传统的基于强度的方法。