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

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/

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BibTeX RIS

BibTeX

@misc{besier2026rnrexm,
  title = {{RnR-ExM: Robust Non-rigid Registration Challenge for Expansion Microscopy Volumes}},
  author = {E. Besier and R. Zhang and Y. Bando and M. Alawi and Y. Quemener and C. Kapoor and M. Hoffmann and A. Dalca and A. Casamitjana and I. Arganda-Carreras and G. Fleishman and X. Jia and J. Duan and T. C. W. Mok and Z. Li and M. E. Schroeder and E. Boyden and H. Pfister and D. Wei},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.22.760871},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.22.760871v1},
  note = {CC BY-NC-ND},
}

RIS

TY  - UNPB
TI  - RnR-ExM: Robust Non-rigid Registration Challenge for Expansion Microscopy Volumes
AU  - E. Besier
AU  - R. Zhang
AU  - Y. Bando
AU  - M. Alawi
AU  - Y. Quemener
AU  - C. Kapoor
AU  - M. Hoffmann
AU  - A. Dalca
AU  - A. Casamitjana
AU  - I. Arganda-Carreras
AU  - G. Fleishman
AU  - X. Jia
AU  - J. Duan
AU  - T. C. W. Mok
AU  - Z. Li
AU  - M. E. Schroeder
AU  - E. Boyden
AU  - H. Pfister
AU  - D. Wei
PY  - 2026
DA  - 2026/10/22
PB  - bioRxiv
DO  - 10.64898/2026.10.22.760871
UR  - https://www.biorxiv.org/content/10.64898/2026.10.22.760871v1
AB  - 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/
SN  - 2692-8205
ER  -

Abstract

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