Rotation-invariant graph transformers enable global bioimage registration and cell matching

Cell matching is a key step in the joint analysis of different imaging modalities in biology. Its main challenges stem from large differences in sample orientation and geometry, local deformations, missing objects, and variations in image appearance and resolution. Commonly used registration pipelines therefore rely heavily on manual or heuristic pre-alignment. We propose grreg, a GNN-based approach for learning context-aware cellular representations that enable direct matching across samples. It combines a rotation- and translation-invariant GCN for local feature extraction and a graph transformer with two kinds of attention: distance-aware self-attention to improve the receptive field of local descriptors, and cross-attention to enable cross-image communication. Our method reaches high matching accuracy, does not require pre-alignment and scales to samples with over 30000 objects through a sparse attention mechanism, as demonstrated on zebrafish embryo development trajectories from Zebrafish Hub. We also introduce a new cross-modal benchmark Platy5+1 to aid further development of bioimage registration.