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Two datasets that used chunkflow in the image processing pipeline are publicly available at https://microns-explorer.org/ and https://flywire.ai/.
Source code and documentation is available as Supplementary Software and online at https://github.com/seung-lab/chunkflow.
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We would like to thank T. Macrina for realigning the somatosensory cortex dataset. We would also like to thank W. Wong for discussions and N. Kemnitz for cloud deployment help. We are grateful to Google for providing the technical support and computational resources, including early access to NVIDIA T4 GPUs on the Google Cloud Platform. We are grateful for technical assistance from Google, Amazon and Intel. These companies were not involved in the design of this study. This research was supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DoI/IBC) contract number D16PC0005, NIH/NIMH (U01MH114824, U01MH117072, RF1MH117815), NIH/NINDS (U19NS104648, R01NS104926), NIH/NEI (R01EY027036), ARO (W911NF-12-1-0594) and the Mathers Foundation. The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/IBC or the US Government.
H.S.S. declares financial interests in Certerra and Zetta AI.
Peer review information Nature Methods thanks Pavel Tomancak and the other, anonymous reviewer(s) for their contribution to the peer review of this work.
Supplementary Note, Supplementary Figs. 1–7 and Supplementary Table 1
Code and documentation for chunkflow
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Wu, J., Silversmith, W.M., Lee, K. et al. Chunkflow: hybrid cloud processing of large 3D images by convolutional nets. Nat Methods 18, 328–330 (2021). https://doi.org/10.1038/s41592-021-01088-5