Adaptive Milling: imaging feedback-driven automated fabrication of consistently thin cryo-lamellae

Abstract

Cryo-electron tomography of 100-200 nm lamellae prepared by focused ion beam milling enables structural analysis of macromolecules within cells, tissues, and small organisms, but lamella preparation remains time-consuming and limits throughput. Although automated workflows enable unsupervised fabrication, they commonly produce variable results because fixed, pre-calibrated parameters are applied without accounting for lamella-to-lamella variability. To overcome these limitations, we developed an Adaptive Milling approach that adapts milling parameters based on imaging feedback. We implemented this approach for the critical polishing stage as Adaptive Polishing, which uses machine-learning-based segmentation of scanning electron microscopy images to identify lamella features predictive of imminent collapse. These are used to periodically assess whether to continue or stop thinning during polishing. We performed a systematic comparison of the quality of lamellae prepared with Adaptive Polishing and conventional automation, and demonstrate that Adaptive Polishing produces thinner lamellae with greater consistency in thickness, and performs well for in situ cryo-electron tomography. Adaptive Polishing was tested on three different microscopes with minimal model retraining required, demonstrating transferability of the approach. We implemented Adaptive Polishing as an open-source plugin for fibsemOS together with machine-learning model training software and pre-trained models.

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BibTeX RIS

BibTeX

@misc{glen2026adaptive,
  title = {{Adaptive Milling: imaging feedback-driven automated fabrication of consistently thin cryo-lamellae}},
  author = {T. Glen and T. M. Fish and E. M. L. Ho and P. Cleeve and L. M. A. Perdigao and N. Gamage and R. T. Ravi and J. B. Gilchrist and M. C. Darrow and M. Basham and M. Dumoux and C. Berger},
  howpublished = {bioRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.20.752565},
  url = {https://www.biorxiv.org/content/10.64898/2026.09.20.752565v1},
  note = {CC BY},
}

RIS

TY  - UNPB
TI  - Adaptive Milling: imaging feedback-driven automated fabrication of consistently thin cryo-lamellae
AU  - T. Glen
AU  - T. M. Fish
AU  - E. M. L. Ho
AU  - P. Cleeve
AU  - L. M. A. Perdigao
AU  - N. Gamage
AU  - R. T. Ravi
AU  - J. B. Gilchrist
AU  - M. C. Darrow
AU  - M. Basham
AU  - M. Dumoux
AU  - C. Berger
PY  - 2026
DA  - 2026/09/20
PB  - bioRxiv
DO  - 10.64898/2026.09.20.752565
UR  - https://www.biorxiv.org/content/10.64898/2026.09.20.752565v1
AB  - Cryo-electron tomography of 100-200 nm lamellae prepared by focused ion beam milling enables structural analysis of macromolecules within cells, tissues, and small organisms, but lamella preparation remains time-consuming and limits throughput. Although automated workflows enable unsupervised fabrication, they commonly produce variable results because fixed, pre-calibrated parameters are applied without accounting for lamella-to-lamella variability. To overcome these limitations, we developed an Adaptive Milling approach that adapts milling parameters based on imaging feedback. We implemented this approach for the critical polishing stage as Adaptive Polishing, which uses machine-learning-based segmentation of scanning electron microscopy images to identify lamella features predictive of imminent collapse. These are used to periodically assess whether to continue or stop thinning during polishing. We performed a systematic comparison of the quality of lamellae prepared with Adaptive Polishing and conventional automation, and demonstrate that Adaptive Polishing produces thinner lamellae with greater consistency in thickness, and performs well for in situ cryo-electron tomography. Adaptive Polishing was tested on three different microscopes with minimal model retraining required, demonstrating transferability of the approach. We implemented Adaptive Polishing as an open-source plugin for fibsemOS together with machine-learning model training software and pre-trained models.
ER  -

Abstract

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