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  1. Article ; Online: MoBIE: a Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data.

    Pape, Constantin / Meechan, Kimberly / Moreva, Ekaterina / Schorb, Martin / Chiaruttini, Nicolas / Zinchenko, Valentyna / Martinez Vergara, Hernando / Mizzon, Giulia / Moore, Josh / Arendt, Detlev / Kreshuk, Anna / Schwab, Yannick / Tischer, Christian

    Nature methods

    2024  Volume 20, Issue 4, Page(s) 475–476

    MeSH term(s) Fiji ; Image Processing, Computer-Assisted/methods ; Software ; Big Data
    Language English
    Publishing date 2024-03-01
    Publishing country United States
    Document type Letter ; Research Support, Non-U.S. Gov't
    ZDB-ID 2169522-2
    ISSN 1548-7105 ; 1548-7091
    ISSN (online) 1548-7105
    ISSN 1548-7091
    DOI 10.1038/s41592-023-01776-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online ; Thesis: Scalable Instance Segmentation for Microscopy

    Pape, Constantin [Verfasser] / Hamprecht, Fred [Akademischer Betreuer]

    2021  

    Author's details Constantin Pape ; Betreuer: Fred Hamprecht
    Keywords Biowissenschaften, Biologie ; Life Science, Biology
    Subject code sg570
    Language English
    Publisher Universitätsbibliothek Heidelberg
    Publishing place Heidelberg
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

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  3. Book ; Online: Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddings

    Wolny, Adrian / Yu, Qin / Pape, Constantin / Kreshuk, Anna

    2021  

    Abstract: Most state-of-the-art instance segmentation methods have to be trained on densely annotated images. While difficult in general, this requirement is especially daunting for biomedical images, where domain expertise is often required for annotation and no ... ...

    Abstract Most state-of-the-art instance segmentation methods have to be trained on densely annotated images. While difficult in general, this requirement is especially daunting for biomedical images, where domain expertise is often required for annotation and no large public data collections are available for pre-training. We propose to address the dense annotation bottleneck by introducing a proposal-free segmentation approach based on non-spatial embeddings, which exploits the structure of the learned embedding space to extract individual instances in a differentiable way. The segmentation loss can then be applied directly to instances and the overall pipeline can be trained in a fully- or weakly supervised manner. We consider the challenging case of positive-unlabeled supervision, where a novel self-supervised consistency loss is introduced for the unlabeled parts of the training data. We evaluate the proposed method on 2D and 3D segmentation problems in different microscopy modalities as well as on the Cityscapes and CVPPP instance segmentation benchmarks, achieving state-of-the-art results on the latter. The code is available at: https://github.com/kreshuklab/spoco

    Comment: CVPR 2022 camera-ready
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 004
    Publishing date 2021-03-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Volume electron microscopy.

    Peddie, Christopher J / Genoud, Christel / Kreshuk, Anna / Meechan, Kimberly / Micheva, Kristina D / Narayan, Kedar / Pape, Constantin / Parton, Robert G / Schieber, Nicole L / Schwab, Yannick / Titze, Benjamin / Verkade, Paul / Aubrey, Aubrey / Collinson, Lucy M

    Nature reviews. Methods primers

    2023  Volume 2, Page(s) 51

    Abstract: Life exists in three dimensions, but until the turn of the century most electron microscopy methods provided only 2D image data. Recently, electron microscopy techniques capable of delving deep into the structure of cells and tissues have emerged, ... ...

    Abstract Life exists in three dimensions, but until the turn of the century most electron microscopy methods provided only 2D image data. Recently, electron microscopy techniques capable of delving deep into the structure of cells and tissues have emerged, collectively called volume electron microscopy (vEM). Developments in vEM have been dubbed a quiet revolution as the field evolved from established transmission and scanning electron microscopy techniques, so early publications largely focused on the bioscience applications rather than the underlying technological breakthroughs. However, with an explosion in the uptake of vEM across the biosciences and fast-paced advances in volume, resolution, throughput and ease of use, it is timely to introduce the field to new audiences. In this Primer, we introduce the different vEM imaging modalities, the specialized sample processing and image analysis pipelines that accompany each modality and the types of information revealed in the data. We showcase key applications in the biosciences where vEM has helped make breakthrough discoveries and consider limitations and future directions. We aim to show new users how vEM can support discovery science in their own research fields and inspire broader uptake of the technology, finally allowing its full adoption into mainstream biological imaging.
    Language English
    Publishing date 2023-06-06
    Publishing country England
    Document type Journal Article
    ISSN 2662-8449
    ISSN (online) 2662-8449
    DOI 10.1038/s43586-022-00131-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: Leveraging Domain Knowledge to improve EM image segmentation with Lifted Multicuts

    Pape, Constantin / Matskevych, Alex / Hennies, Julian / Kreshuk, Anna

    2019  

    Abstract: The throughput of electron microscopes has increased significantly in recent years, enabling detailed analysis of cell morphology and ultrastructure. Analysis of neural circuits at single-synapse resolution remains the flagship target of this technique, ... ...

    Abstract The throughput of electron microscopes has increased significantly in recent years, enabling detailed analysis of cell morphology and ultrastructure. Analysis of neural circuits at single-synapse resolution remains the flagship target of this technique, but applications to cell and developmental biology are also starting to emerge at scale. The amount of data acquired in such studies makes manual instance segmentation, a fundamental step in many analysis pipelines, impossible. While automatic segmentation approaches have improved significantly thanks to the adoption of convolutional neural networks, their accuracy still lags behind human annotations and requires additional manual proof-reading. A major hindrance to further improvements is the limited field of view of the segmentation networks preventing them from exploiting the expected cell morphology or other prior biological knowledge which humans use to inform their segmentation decisions. In this contribution, we show how such domain-specific information can be leveraged by expressing it as long-range interactions in a graph partitioning problem known as the lifted multicut problem. Using this formulation, we demonstrate significant improvement in segmentation accuracy for three challenging EM segmentation problems from neuroscience and cell biology.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2019-05-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning.

    Wolf, Steffen / Bailoni, Alberto / Pape, Constantin / Rahaman, Nasim / Kreshuk, Anna / Kothe, Ullrich / Hamprecht, Fred A

    IEEE transactions on pattern analysis and machine intelligence

    2021  Volume 43, Issue 10, Page(s) 3724–3738

    Abstract: Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task ... ...

    Abstract Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task as multicut / correlation clustering, an NP-hard problem. Here, we propose an efficient algorithm for graph partitioning, the "Mutex Watershed". Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold. We also prove that this simple algorithm solves to global optimality an objective function that is intimately related to the multicut / correlation clustering integer linear programming formulation. The algorithm is deterministic, very simple to implement, and has empirically linearithmic complexity. When presented with short-range attractive and long-range repulsive cues from a deep neural network, the Mutex Watershed gives the best results currently known for the competitive ISBI 2012 EM segmentation benchmark.
    Language English
    Publishing date 2021-09-02
    Publishing country United States
    Document type Journal Article
    ISSN 1939-3539
    ISSN (online) 1939-3539
    DOI 10.1109/TPAMI.2020.2980827
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Microglia complement signaling promotes neuronal elimination and normal brain functional connectivity.

    Deivasigamani, Senthilkumar / Miteva, Mariya T / Natale, Silvia / Gutierrez-Barragan, Daniel / Basilico, Bernadette / Di Angelantonio, Silvia / Weinhard, Laetitia / Molotkov, Dmitry / Deb, Sukrita / Pape, Constantin / Bolasco, Giulia / Galbusera, Alberto / Asari, Hiroki / Gozzi, Alessandro / Ragozzino, Davide / Gross, Cornelius T

    Cerebral cortex (New York, N.Y. : 1991)

    2023  Volume 33, Issue 21, Page(s) 10750–10760

    Abstract: Complement signaling is thought to serve as an opsonization signal to promote the phagocytosis of synapses by microglia. However, while its role in synaptic remodeling has been demonstrated in the retino-thalamic system, it remains unclear whether ... ...

    Abstract Complement signaling is thought to serve as an opsonization signal to promote the phagocytosis of synapses by microglia. However, while its role in synaptic remodeling has been demonstrated in the retino-thalamic system, it remains unclear whether complement signaling mediates synaptic pruning in the brain more generally. Here we found that mice lacking the Complement receptor 3, the major microglia complement receptor, failed to show a deficit in either synaptic pruning or axon elimination in the developing mouse cortex. Instead, mice lacking Complement receptor 3 exhibited a deficit in the perinatal elimination of neurons in the cortex, a deficit that is associated with increased cortical thickness and enhanced functional connectivity in these regions in adulthood. These data demonstrate a role for complement in promoting neuronal elimination in the developing cortex.
    MeSH term(s) Mice ; Animals ; Microglia ; Neurons ; Brain ; Signal Transduction ; Synapses/physiology ; Receptors, Complement ; Neuronal Plasticity/physiology
    Chemical Substances Receptors, Complement
    Language English
    Publishing date 2023-09-16
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1077450-6
    ISSN 1460-2199 ; 1047-3211
    ISSN (online) 1460-2199
    ISSN 1047-3211
    DOI 10.1093/cercor/bhad313
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Convolutional networks for supervised mining of molecular patterns within cellular context.

    de Teresa-Trueba, Irene / Goetz, Sara K / Mattausch, Alexander / Stojanovska, Frosina / Zimmerli, Christian E / Toro-Nahuelpan, Mauricio / Cheng, Dorothy W C / Tollervey, Fergus / Pape, Constantin / Beck, Martin / Diz-Muñoz, Alba / Kreshuk, Anna / Mahamid, Julia / Zaugg, Judith B

    Nature methods

    2023  Volume 20, Issue 2, Page(s) 284–294

    Abstract: Cryo-electron tomograms capture a wealth of structural information on the molecular constituents of cells and tissues. We present DeePiCt (deep picker in context), an open-source deep-learning framework for supervised segmentation and macromolecular ... ...

    Abstract Cryo-electron tomograms capture a wealth of structural information on the molecular constituents of cells and tissues. We present DeePiCt (deep picker in context), an open-source deep-learning framework for supervised segmentation and macromolecular complex localization in cryo-electron tomography. To train and benchmark DeePiCt on experimental data, we comprehensively annotated 20 tomograms of Schizosaccharomyces pombe for ribosomes, fatty acid synthases, membranes, nuclear pore complexes, organelles, and cytosol. By comparing DeePiCt to state-of-the-art approaches on this dataset, we show its unique ability to identify low-abundance and low-density complexes. We use DeePiCt to study compositionally distinct subpopulations of cellular ribosomes, with emphasis on their contextual association with mitochondria and the endoplasmic reticulum. Finally, applying pre-trained networks to a HeLa cell tomogram demonstrates that DeePiCt achieves high-quality predictions in unseen datasets from different biological species in a matter of minutes. The comprehensively annotated experimental data and pre-trained networks are provided for immediate use by the community.
    MeSH term(s) Humans ; HeLa Cells ; Mitochondria ; Ribosomes ; Electron Microscope Tomography/methods ; Endoplasmic Reticulum ; Image Processing, Computer-Assisted/methods
    Language English
    Publishing date 2023-01-23
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2169522-2
    ISSN 1548-7105 ; 1548-7091
    ISSN (online) 1548-7105
    ISSN 1548-7091
    DOI 10.1038/s41592-022-01746-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Book ; Online: Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks

    Bailoni, Alberto / Pape, Constantin / Wolf, Steffen / Kreshuk, Anna / Hamprecht, Fred A.

    2020  

    Abstract: This work introduces a new proposal-free instance segmentation method that builds on single-instance segmentation masks predicted across the entire image in a sliding window style. In contrast to related approaches, our method concurrently predicts all ... ...

    Abstract This work introduces a new proposal-free instance segmentation method that builds on single-instance segmentation masks predicted across the entire image in a sliding window style. In contrast to related approaches, our method concurrently predicts all masks, one for each pixel, and thus resolves any conflict jointly across the entire image. Specifically, predictions from overlapping masks are combined into edge weights of a signed graph that is subsequently partitioned to obtain all final instances concurrently. The result is a parameter-free method that is strongly robust to noise and prioritizes predictions with the highest consensus across overlapping masks. All masks are decoded from a low dimensional latent representation, which results in great memory savings strictly required for applications to large volumetric images. We test our method on the challenging CREMI 2016 neuron segmentation benchmark where it achieves competitive scores.

    Comment: Presented at GCPR 2020
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Publishing date 2020-09-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies.

    Moore, Josh / Allan, Chris / Besson, Sébastien / Burel, Jean-Marie / Diel, Erin / Gault, David / Kozlowski, Kevin / Lindner, Dominik / Linkert, Melissa / Manz, Trevor / Moore, Will / Pape, Constantin / Tischer, Christian / Swedlow, Jason R

    Nature methods

    2021  Volume 18, Issue 12, Page(s) 1496–1498

    Abstract: The rapid pace of innovation in biological imaging and the diversity of its applications have prevented the establishment of a community-agreed standardized data format. We propose that complementing established open formats such as OME-TIFF and HDF5 ... ...

    Abstract The rapid pace of innovation in biological imaging and the diversity of its applications have prevented the establishment of a community-agreed standardized data format. We propose that complementing established open formats such as OME-TIFF and HDF5 with a next-generation file format such as Zarr will satisfy the majority of use cases in bioimaging. Critically, a common metadata format used in all these vessels can deliver truly findable, accessible, interoperable and reusable bioimaging data.
    MeSH term(s) Benchmarking ; Computational Biology/instrumentation ; Computational Biology/methods ; Computational Biology/standards ; Data Compression ; Databases, Factual ; Information Storage and Retrieval ; Internet ; Metadata ; Microscopy/instrumentation ; Microscopy/methods ; Microscopy/standards ; Programming Languages ; SARS-CoV-2 ; Software
    Language English
    Publishing date 2021-11-29
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2169522-2
    ISSN 1548-7105 ; 1548-7091
    ISSN (online) 1548-7105
    ISSN 1548-7091
    DOI 10.1038/s41592-021-01326-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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