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Cited by 220

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Robust registration of calcium images by learned contrast synthesis

Open access
Bogovic, John A., Hanslovsky, Philipp, Wong, Allan, Saalfeld, Stephan·2016·2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

Abstract

Multi-modal image registration is a challenging task that is vital to fuse complementary signals for subsequent analyses. Despite much research into cost functions addressing this challenge, there exist cases in which these are ineffective. In this work, we show that (1) this is true for the registration of in-vivo Drosophila brain volumes visualizing genetically encoded calcium indicators to an nc82 atlas and (2) that machine learning based contrast synthesis can yield improvements. More specifically, the number of subjects for which the registration outright failed was greatly reduced (from 40% to 15%) by using a synthesized image.

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Metadata

Authors
Bogovic, J. A., Hanslovsky, P., Wong, A., & Saalfeld, S.
Venue
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
Publisher
IEEE
Published
2016-04
Pages
1123-1126
OpenAlex
W2228548648
Cited by
220
References
2
FWCI
1.06
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Robust registration of calcium images by learned contrast synthesis. https://app.cochat.ai/papers/10.1109/isbi.2016.7493463
The paper
Bogovic, J. A., Hanslovsky, P., Wong, A., & Saalfeld, S. (2016). Robust registration of calcium images by learned contrast synthesis. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). https://doi.org/10.1109/isbi.2016.7493463

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.