Abstract Background Multiscale information integration is essential for a comprehensive understanding of brain structure and function [1]. Optical microscopy provides mesoscopic information on brain region distribution, neuronal projections, and functional activity patterns, while electron microscopy offers microscopic details, such as cell morphology and synaptic connectivity. Aligning cell nucleus information from these two modalities is therefore critical for linking global tissue organization with cellular-level mechanisms. However, fundamental differences in imaging principles and data characteristics make cross-modal cell nucleus point clouds registration highly challenging [2]. Variations in point density, inconsistent noise levels, and complex nonlinear deformations often prevent traditional methods from achieving the accuracy and biological plausibility required in neuroscience research. Method We propose a non-rigid registration strategy for cross-modal cell nucleus point clouds. The method adopts a multi-level block correspondence scheme to achieve consistent alignment across local and global scales, while integrating neighborhood constraints and a bidirectional weighting mechanism to mitigate the influence of outliers and data sparsity. Experiment Experimental results demonstrate that this strategy significantly improves registration accuracy and robustness while preserving structural continuity. This work provides technical support for cross-modal neural tissue mapping and demonstrates the potential of multiscale information integration to advance brain connectomics and neurological disease research. References [1] Shapson-Coe A, Januszewski M, Berger D R, et al. ‘A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution’ [J]. Science, 2024, 384(6696). [2] Huang X, Mei G, Zhang J. ‘Cross-source point cloud registration: Challenges, progress and prospects’ [J]. Neurocomputing, 2023, 548: 126383.

Pyramid-based deformation decomposition is a promising registration framework, which gradually decomposes the deformation field into multi-resolution subfields for precise registration. However, most pyramid-based methods directly produce one subfield per resolution level, which does not fully depict the spatial deformation. In this paper, we propose a novel registration model, called GroupMorph. Different from typical pyramid-based methods, we adopt the grouping-combination strategy to predict deformation field at each resolution. Specifically, we perform group-wise correlation calculation to measure the similarities of grouped features. After that, n groups of deformation subfields with different receptive fields are predicted in parallel. By composing these subfields, a deformation field with multi-receptive field ranges is formed, which can effectively identify both large and small deformations. Meanwhile, a contextual fusion module is designed to fuse the contextual features and provide the inter-group information for the field estimator of the next level. By leveraging the inter-group correspondence, the synergy among deformation subfields is enhanced. Extensive experiments on four public datasets demonstrate the effectiveness of GroupMorph. Code is available at https://github.com/TVayne/GroupMorph.

MOTIVATION: The registration of serial section electron microscope images is a critical step in reconstructing biological tissue volumes, and it aims to eliminate complex nonlinear deformations from sectioning and replicate the correct neurite structure. However, due to the inherent properties of biological structures and the challenges posed by section preparation of biological tissues, achieving an accurate registration of serial sections remains a significant challenge. Conventional nonlinear registration techniques, which are effective in eliminating nonlinear deformation, can also eliminate the natural morphological variation of neurites across sections. Additionally, accumulation of registration errors alters the neurite structure. RESULTS: This article proposes a novel method for serial section registration that utilizes an unsupervised optical flow network to measure feature similarity rather than pixel similarity to eliminate nonlinear deformation and achieve pairwise registration between sections. The optical flow network is then employed to estimate and compensate for cumulative registration error, thereby allowing for the reconstruction of the structure of biological tissues. Based on the novel serial section registration method, a serial split technique is proposed for long-serial sections. Experimental results demonstrate that the state-of-the-art method proposed here effectively improves the spatial continuity of serial sections, leading to more accurate registration and improved reconstruction of the structure of biological tissues. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/TongXin-CASIA/EFSR.

Affine registration can fit the non-rigid deformation of slices effectively, and it is widely used in volume reconstruction of biological tissue. But most of the existing affine registration methods are registered in a given sequence, which results in the accumulation of errors. In this paper, a global optimized affine registration method is proposed, which can be used in volume reconstruction. To eliminate the cumulative error, the affine transformation of all images is estimated simultaneously based on an energy function. A soft penalty on affine transformation is added to restrict the shearing of images. Experiments show that our method provides a more reliable registration result compared with sequential affine registration. It can solve the problems caused by the accumulation of errors. The registration result fits the deformation of slices well and preserves the rigidity of images.