Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
Ministry of Education Key Laboratory of Cell Proliferation and Differentiation and State Key Laboratory of Biomembrane and Membrane Biotechnology, College of Life Sciences, Peking University, Beijing 100871, China; School of Psychological and Cognitive Sciences, Peking University, Beijing 100871, China; IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
Neuroscience. 2022 May 21;491:1-12. doi: 10.1016/j.neuroscience.2022.03.034. Epub 2022 Mar 30.
Accurate and efficient non-rigid registration is important to investigate neural mechanisms in multi-session two-photon (2p) imaging across a few days. The 2p imaging recordings from different sessions usually possess certain complex misalignment or huge data variance due to relocation errors during experimental operations or brain recovery. Most of the reported neural image registration tools were able to solve the registration problem in the same session with small deformation. However, the registration of neural images across multi-sessions remains a challenge. In this study, we report the development of a non-rigid registration method for 2p imaging in mice based on image triangulation and piecewise affine transformation (TPAT) technologies. The TPAT method supported both automatic and semi-automatic operation types, and both showed great performance in the benchmark test of non-rigid neural image registration. The proposed method constitutes a step forward in promoting and accelerating discoveries from multi-session 2p imaging research.
准确高效的非刚性配准对于研究多日双光子（2p）成像中的神经机制非常重要。由于实验操作或大脑恢复期间的重新定位误差，不同时间点的 2p 成像记录通常存在一定的复杂错位或巨大的数据差异。大多数报道的神经图像配准工具都能够解决同一会话中小变形的配准问题。然而，跨多会话的神经图像配准仍然是一个挑战。在这项研究中，我们报告了一种基于图像三角化和分段仿射变换（TPAT）技术的小鼠 2p 成像非刚性配准方法的开发。TPAT 方法支持自动和半自动操作类型，在非刚性神经图像配准的基准测试中均表现出出色的性能。该方法在促进和加速多会话 2p 成像研究的发现方面迈出了一步。
