Author

C. J. Peddie

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cell Biology· 4 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Assay:imaging· 4 Approach:experimental assay· 3 Organism:human· 3 Research Focus:cell type annotation· 2 Research Focus:clustering· 2 Approach:deep learning· 2 Assay:electronic health records· 2 Research Focus:cell-cell communication· 1

Frequent Co-Authors

M. L. Jones· 5 shared L. M. Collinson· 4 shared A. E. Weston· 2 shared C. C. Reyes-Aldasoro· 2 shared C. Karabag· 2 shared A. Milberger· 1 shared A. Riedinger· 1 shared A. Strange· 1 shared

Activity by Year

2020-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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5 shared papersCell Biology

M. L. Jones

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Author profile →

5 papers

Recent·Most cited

CLEM-Reg: An automated point cloud based registration algorithm for correlative light and volume electron microscopy

D. Krentzel, M. Elphick, M.-C. Domart, C. J. Peddie, R. F. Laine, C. Shand, R. Henriques, L. M. Collinson, M. L. Jones

CLEM-Reg automates 3D alignment of vCLEM datasets, achieving sub-micron correlation of fluorescent signals with EM structures and enabling identification of TGN46-positive transport carriers.

bioRxiv · May 11, 2023Cell Biology

Crosshair, semi-automated targeting for electron microscopy with a motorised ultramicrotome

K. Meechan, W. Guan, A. Riedinger, V. Stankova, A. Yoshimura, R. Pipitone, A. Milberger, H. Schaar, I. Romero-Brey, R. Templin, C. J. Peddie, N. L. Schieber, M. L. Jones, L. Collinson, Y. Schwab

The paper introduces a semi-automated workflow for targeting regions of interest in resin-embedded samples using a motorised ultramicrotome and an open-source Fiji plugin called Crosshair.

bioRxiv · Jun 2, 2022Cell Biology

Citizen science, cells and CNNs - deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

H. Spiers, H. Songhurst, L. Nightingale, J. de Folter, R. Hutchings, C. J. Peddie, A. Weston, A. Strange, S. Hindmarsh, C. Lintott, L. M. Collinson, M. L. Jones

This paper trains a deep learning model to automatically segment the nuclear envelope in electron microscopy data using volunteer annotations from a citizen science project.

bioRxiv · Jul 28, 2020Cell Biology

Geometric differences between nuclear envelopes of Wild-type and Chlamydia trachomatis-infected HeLa cells

C. Karabag, M. L. Jones, C. J. Peddie, A. E. Weston, L. M. Collinson, C. C. Reyes-Aldasoro

The paper compares geometric features of nuclear envelopes in wild-type and Chlamydia trachomatis-infected HeLa cells using electron microscopy and spheroid modeling.

bioRxiv · Apr 2, 2020 · PreprintCell Biology

Semantic Segmentation of HeLa Cells: An Objective Comparison between one Traditional Algorithm and Three Deep-Learning Architectures

C. Karabag, M. L. Jones, C. J. Peddie, A. E. Weston, L. M. Collinson, C. C. Reyes-Aldasoro

This paper compares a traditional image-processing algorithm with three deep learning models for semantic segmentation of HeLa cells, finding the traditional method outperforms all deep learning architectures.

bioRxiv · Mar 5, 2020Cancer Biology