RnR-ExM: Robust Non-rigid Registration Challenge for Expansion Microscopy Volumes

Authors

Besier, E., Zhang, R., Bando, Y., Alawi, M., Quemener, Y., Kapoor, C., Hoffmann, M., Dalca, A., Casamitjana, A., Arganda-Carreras, I., Fleishman, G., Jia, X., Duan, J., Mok, T. C. W., Li, Z., Schroeder, M. E., Boyden, E., Pfister, H., Wei, D.

Abstract

Expansion Microscopy (ExM) enables nanoscale imaging of biological samples while preserving structural and molecular details. The Robust Non-rigid Registration Challenge for ExM (RnR-ExM) was launched to benchmark 3D registration algorithms on real-world ExM datasets. The challenge released 24 pairs of high-resolution 3D volumes from zebrafish, mouse, and C.~elegans, deliberately selected for their difficulty with standard alignment methods. To evaluate performance, we provided curated segmentation masks of nuclei and blood vessels as landmarks for half of the dataset. To encourage learning-based approaches, we released code for generating synthetic training data reflecting the deformation and appearance of the unlabeled pairs. By submission, 168 participants registered, with six teams submitting results by the deadline; the top three were invited to present at ISBI 2023. Accurate registration of ExM volumes is essential for downstream biological analyses such as multiplexed molecular mapping and cell-type-specific structural profiling. In this paper, we detail the methods and results of these top teams, highlighting current capabilities and future directions for 3D registration in ExM. All datasets and code are available at https://rnr-exm.grand-challenge.org/

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 26 Oct 2026.

Stats

1-terrible, 9-excellent. How would you rate this preprint? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.