Main Menu (Mobile)- Block

Main Menu - Block

custom | custom

Search Results

filters_region_cap | custom

Filter

facetapi-Q2b17qCsTdECvJIqZJgYMaGsr8vANl1n | block

Associated Lab

facetapi-W9JlIB1X0bjs93n1Alu3wHJQTTgDCBGe | block

Associated Project Team

facetapi-61yz1V0li8B1bixrCWxdAe2aYiEXdhd0 | block

Associated Support Team

facetapi-PV5lg7xuz68EAY8eakJzrcmwtdGEnxR0 | block

Publication Date

general_search_page-panel_pane_1 | views_panes

12 Janelia Publications

Showing 11-12 of 12 results
Your Criteria:
    Funke LabHess LabLippincott-Schwartz LabSaalfeld LabCellMap
    07/22/23 | Towards Generalizable Organelle Segmentation in Volume Electron Microscopy.
    Heinrich L, Patton W, Bennett D, Ackerman D, Park G, Bogovic JA, Eckstein N, Petruncio A, Clements J, Pang S, Shan Xu C, Funke J, Korff W, Hess H, Lippincott-Schwartz J, Saalfeld S, Weigel A, CellMap Project Team
    Microscopy and Microanalysis. 2023 Jul 22;29(Supplement_1):975. doi: 10.1093/micmic/ozad067.487
    Funke Lab
    10/01/23 | Unsupervised Learning of Object-Centric Embeddings for Cell Instance Segmentation in Microscopy Images
    Wolf S, Lalit M, McDole K, Funke J
    2023 IEEE/CVF International Conference on Computer Vision (ICCV). 2023 Oct 01:. doi: 10.1109/ICCV51070.2023.01944

    Segmentation of objects in microscopy images is required for many biomedical applications. We introduce object-centric embeddings (OCEs), which embed image patches such that the spatial offsets between patches cropped from the same object are preserved. Those learnt embeddings can be used to delineate individual objects and thus obtain instance segmentations. Here, we show theoretically that, under assumptions commonly found in microscopy images, OCEs can be learnt through a self-supervised task that predicts the spatial offset between image patches. Together, this forms an unsupervised cell instance segmentation method which we evaluate on nine diverse large-scale microscopy datasets. Segmentations obtained with our method lead to substantially improved results, compared to state-of-the-art baselines on six out of nine datasets, and perform on par on the remaining three datasets. If ground-truth annotations are available, our method serves as an excellent starting point for supervised training, reducing the required amount of ground-truth needed by one order of magnitude, thus substantially increasing the practical applicability of our method. Source code is available at github.com/funkelab/cellulus.

    View Publication Page