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format_version: 0.2.2
type: collection
name: ZeroCost4Mic Collection
tags: [zero, bioimage.io]
description: "Resources for BioImgage.IO curated by the ZeroCost4Mic team."
authors: []
documentation: https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki
maintainers: []
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021)"
doi: 10.1038/s41467-021-22518-0
config:
id: zero
name: ZeroCostDL4Mic
version: 1.7.1
tags:
- ZeroCostDL4Mic
logo: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/ZeroCostLogo.png
icon: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/ZeroCostLogo.png
splash_title: ZeroCostDL4Mic
splash_subtitle: A Google Colab based no-cost toolbox to explore Deep-Learning in Microscopy
splash_feature_list: []
explore_button_text: Start Exploring
background_image: static/img/zoo-background.svg
resource_types:
- model
- application
- dataset
default_type: application
url_root: https://raw.githubusercontent.com/oeway/ZeroCostDL4Mic/master
#------------------------------------- ZeroCostDL4Mic Datasets ---------------------------------------------
collection:
# see here for the format: https://bioimage.io/#/?show=contribute
# replace this with your actual dataset
- type: dataset
id: Dataset_StarDist_2D_ZeroCostDL4Mic_2D
name: StarDist (2D) example training and test dataset - ZeroCostDL4Mic
description: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
authors: [Johanna Jukkala, Guillaume Jacquemet]
documentation: >-
https://doi.org/10.5281/zenodo.3715492
tags: [StarDist, segmentation, ZeroCostDL4Mic, 2D]
download_url: https://doi.org/10.5281/zenodo.3715492
covers:
- https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/Wiki_files/Stardist_nuclei_masks.png
- type: dataset
id: Dataset_Noise2Void_2D_ZeroCostDL4Mic
name: Noise2Void (2D) example training and test dataset - ZeroCostDL4Mic
description: Fluorescence microscopy (paxillin-GFP)
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
authors: [Aki Stubb, Guillaume Jacquemet, Johanna Ivaska]
documentation: >-
https://doi.org/10.5281/zenodo.3713315
tags: [Noise2Void, denoising, ZeroCostDL4Mic, 2D]
download_url: https://doi.org/10.5281/zenodo.3713315
covers:
- https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/Wiki_files/N2V_wiki.png
- type: dataset
id: Dataset_Noise2Void_3D_ZeroCostDL4Mic
name: Noise2Void (3D) example training and test dataset - ZeroCostDL4Mic
description: Fluorescence microscopy (Lifeact-RFP)
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
authors: [Guillaume Jacquemet]
documentation: >-
https://doi.org/10.5281/zenodo.3713326
tags: [Noise2Void, denoising, ZeroCostDL4Mic, 3D]
download_url: https://doi.org/10.5281/zenodo.3713326
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/N2V_3D_dataset.png
- type: dataset
id: Dataset_CARE_2D_ZeroCostDL4Mic
name: CARE (2D) example training and test dataset - ZeroCostDL4Mic
description: Fluorescence microscopy (Lifeact-RFP)
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
authors: [Guillaume Jacquemet]
documentation: >-
https://doi.org/10.5281/zenodo.3713330
tags: [CARE, denoising, ZeroCostDL4Mic, 2D]
download_url: https://doi.org/10.5281/zenodo.3713330
covers:
- https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/Wiki_files/CARE_wiki.png
- type: dataset
id: Dataset_CARE_3D_ZeroCostDL4Mic
name: CARE (3D) example training and test dataset - ZeroCostDL4Mic
description: Fluorescence microscopy (Lifeact-RFP)
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
authors: [Guillaume Jacquemet]
documentation: >-
https://doi.org/10.5281/zenodo.3713337
tags: [CARE, denoising, ZeroCostDL4Mic, 3D]
download_url: https://doi.org/10.5281/zenodo.3713337
covers:
- https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/Wiki_files/CARE_wiki.png
- type: dataset
id: Dataset_fnet_3D_ZeroCostDL4Mic
name: Label-free prediction (fnet) example training and test dataset - ZeroCostDL4Mic
description: Confocal microscopy data (TOM20 labeled with Alexa Fluor 594)
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
authors: [Christoph Spahn]
documentation: >-
https://doi.org/10.5281/zenodo.3748967
tags: [fnet, labelling, ZeroCostDL4Mic, 3D]
download_url: https://doi.org/10.5281/zenodo.3748967
covers:
- https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/Wiki_files/Fnet_exemplary_data_mitochondria.png
- type: dataset
id: Dataset_Deep-STORM_ZeroCostDL4Mic
name: Deep-STORM training and example dataset - ZeroCostDL4Mic
description: Time-series of simulated, randomly distributed single-molecule localization (SMLM) data (Training dataset). Experimental time-series dSTORM acquisition of Glial cells stained with phalloidin for actin (Example dataset).
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
authors: [Christophe Leterrier, Romain F. Laine]
documentation: >-
https://doi.org/10.5281/zenodo.3959089
tags: [SMLM, Deep-STORM, ZeroCostDL4Mic, 2D]
download_url: https://doi.org/10.5281/zenodo.3959089
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/DeepSTORM_dataset.png
- type: dataset
id: Dataset_CycleGAN_ZeroCostDL4Mic
name: CycleGAN example training and test dataset - ZeroCostDL4Mic
description: Unpaired microscopy images (fluorescence) of microtubules (Spinning-disk and SRRF reconstructed images)
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
authors: [Guillaume Jacquemet]
documentation: >-
https://doi.org/10.5281/zenodo.3941884
tags: [CycleGAN, ZeroCostDL4Mic]
download_url: https://doi.org/10.5281/zenodo.3941884
covers:
- https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/BioimageModelZoo/Images/CycleGAN_dataset.png
- type: dataset
id: Dataset_pix2pix_ZeroCostDL4Mic
name: pix2pix example training and test dataset - ZeroCostDL4Mic
description: Paired microscopy images (fluorescence) of lifeact-RFP and sir-DNA
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
authors: [Guillaume Jacquemet]
documentation: >-
https://doi.org/10.5281/zenodo.3941889
tags: [pix2pix, ZeroCostDL4Mic]
download_url: https://doi.org/10.5281/zenodo.3941889
covers:
- https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/BioimageModelZoo/Images/pix2pix_dataset.png
- type: dataset
id: Dataset_YOLOv2_ZeroCostDL4Mic
name: YoloV2 example training and test dataset - ZeroCostDL4Mic
description: 2D grayscale .png images with corresponding bounding box annotations in .xml PASCAL Voc format.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
authors: [Guillaume Jacquemet, Lucas von Chamier]
documentation: >-
https://doi.org/10.5281/zenodo.3941908
tags: [YOLOv2, ZeroCostDL4Mic]
download_url: https://doi.org/10.5281/zenodo.3941908
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/yolo_dataset.png
- type: dataset
id: Dataset_StarDist_Fluo_ZeroCostDL4Mic
name: Combining StarDist and TrackMate example 1 - Breast cancer cell dataset
description: Fluorescence microscopy of Nuclei (SiR-DNA) and masks obtained via manual segmentation
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Elnaz Fazeli, Nathan H. Roy, Gautier Follain, Romain F. Laine, Lucas von Chamier, Pekka E. Hänninen, John E. Eriksson, Jean-Yves Tinevez, Guillaume Jacquemet. Automated cell tracking using StarDist and TrackMate. bioRxiv, 2020. DOI: https://doi.org/10.1101/2020.09.22.306233"
doi: https://doi.org/10.1101/2020.09.22.306233
authors: [Guillaume Jacquemet]
documentation: >-
https://zenodo.org/record/4034976
tags: [StarDist, ZeroCostDL4Mic]
download_url: https://zenodo.org/record/4034976
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/StarDist_trainingfluo_trackmate.png
- type: dataset
id: Dataset_StarDist_brightfield_ZeroCostDL4Mic
name: Combining StarDist and TrackMate example 2 - T cell dataset
description: Paired brightfield images of migrating T cells and corresponding masks
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Elnaz Fazeli, Nathan H. Roy, Gautier Follain, Romain F. Laine, Lucas von Chamier, Pekka E. Hänninen, John E. Eriksson, Jean-Yves Tinevez, Guillaume Jacquemet. Automated cell tracking using StarDist and TrackMate. bioRxiv, 2020. DOI: https://doi.org/10.1101/2020.09.22.306233"
doi: https://doi.org/10.1101/2020.09.22.306233
authors: [Nathan H. Roy, Guillaume Jacquemet]
documentation: >-
https://zenodo.org/record/4034929
tags: [StarDist, ZeroCostDL4Mic]
download_url: https://zenodo.org/record/4034929
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/StarDist_trainingTcells_trackmate.png
- type: dataset
id: Dataset_StarDist_brightfield2_ZeroCostDL4Mic
name: Combining StarDist and TrackMate example 3 - Flow chamber dataset
description: Paired brightfield images of cancer cells and corresponding masks
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Elnaz Fazeli, Nathan H. Roy, Gautier Follain, Romain F. Laine, Lucas von Chamier, Pekka E. Hänninen, John E. Eriksson, Jean-Yves Tinevez, Guillaume Jacquemet. Automated cell tracking using StarDist and TrackMate. bioRxiv, 2020. DOI: https://doi.org/10.1101/2020.09.22.306233"
doi: https://doi.org/10.1101/2020.09.22.306233
authors: [Gautier Follain, Guillaume Jacquemet]
documentation: >-
https://zenodo.org/record/4034939
tags: [StarDist, ZeroCostDL4Mic]
download_url: https://zenodo.org/record/4034939
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/StarDist_trainingflo_trackmate.png
- type: dataset
id: Dataset_StarDist_fluo2_ZeroCostDL4Mic
name: training dataset for automated tracking of MDA-MB-231 and BT20 cells
description: Fluorescence microscopy of Nuclei (SiR-DNA) and masks obtained via manual segmentation
cite:
- text: "Moreno-Layseca P, Jäntti NZ, Godbole R, Sommer C, Jacquemet G, Al-Akhrass H, Conway JRW, Kronqvist P, Kallionpää RE, Oliveira-Ferrer L, Cervero P, Linder S, Aepfelbacher M, Zauber H, Rae J, Parton RG, Disanza A, Scita G, Mayor S, Selbach M, Veltel S, Ivaska J. Cargo-specific recruitment in clathrin- and dynamin-independent endocytosis. Nat Cell Biol. 2021 Oct;23(10):1073-1084. doi: 10.1038/s41556-021-00767-x. Epub 2021 Oct 6. PMID: 34616024."
doi: https://doi.org/10.1038/s41556-021-00767-x
authors: [Hussein Al-Akhrass, Johanna Ivaska, Guillaume Jacquemet]
documentation: >-
https://zenodo.org/record/4811213
tags: [StarDist, ZeroCostDL4Mic]
download_url: https://zenodo.org/record/4811213
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/Moreno_dataset_stardist.png
- type: dataset
id: Dataset_Noisy_Nuclei_ZeroCostDL4Mic
name: Noisy nuclei dataset.
description: This dataset contains a denoising training and test dataset for deep learning applications. The training dataset comprises 20 paired matching noisy and high signal-to-noise images. The test dataset contains five paired matching noisy and high signal-to-noise images. Images are Fluorescence microscopy (SiR-DNA) images acquired using a spinning disk confocal microscope with a 20x 0.8 NA objective.
cite:
- text: "Laine RF, Arganda-Carreras I, Henriques R, Jacquemet G. Avoiding a replication crisis in deep-learning-based bioimage analysis. Nat Methods. 2021 Oct;18(10):1136-1144. doi: 10.1038/s41592-021-01284-3. PMID: 34608322; PMCID: PMC7611896.
"
doi: https://doi.org/10.1038/s41592-021-01284-3
authors: [Guillaume Jacquemet]
documentation: >-
https://zenodo.org/record/5750174
tags: [denoising, CARE, Noise2Void, DecoNoising, ZeroCostDL4Mic]
download_url: https://zenodo.org/record/5750174
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/NoisyNuclei_dataset.png
#------------------------------------- DeepBacs Datasets ---------------------------------------------
- type: dataset
id: Dataset_U-Net_2D_multilabel_DeepBacs
name: Multi-label U-Net training dataset (Bacillus subtilis) - DeepBacs
description: Paired bright field images and segmented binary masks of live E. coli cells.
cite:
- text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
doi: https://doi.org/10.1101/2021.11.03.467152
authors: [Mia Conduit, Séamus Holden]
documentation: https://github.com/HenriquesLab/DeepBacs/wiki
tags: [U-Net, multilabel, segmentation, DeepBacs, 2D]
download_url: https://zenodo.org/record/5639253
covers:
- https://zenodo.org/api/iiif/v2/8ba101cf-97ac-4e68-9fff-4cc130b5ea81:93b67447-8b0a-4053-a522-9c141623ca6f:A_Multilabel_U-Net_example_B.subtilis.png/full/750,/0/default.png
- type: dataset
id: Dataset_U-Net_2D_DeepBacs
name: Escherichia coli bright field segmentation dataset - DeepBacs
description: Paired bright field and segmented mask images of live E. coli cells imaged under bright field.
cite:
- text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
doi: https://doi.org/10.1101/2021.11.03.467152
authors: [Spahn Christoph, Heilemann Mike]
documentation: https://github.com/HenriquesLab/DeepBacs/wiki
tags: [U-Net, segmentation, DeepBacs, 2D]
download_url: https://zenodo.org/record/5550935
covers:
- https://zenodo.org/api/iiif/v2/c6dca42a-62cd-423d-8216-b2ae20dab6a3:790a798b-26b2-425b-b1b6-9545d89daa7d:A_Segmentation_E.coli_large_FoV.png/full/750,/0/default.png
- type: dataset
id: Dataset_StarDist_2D_DeepBacs
name: Mixed segmentation dataset - DeepBacs
description: Mixed training and test images of S. aureus, E. coli and B. subtilis for cell segmentation using StarDist.
cite:
- text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
doi: https://doi.org/10.1101/2021.11.03.467152
authors: [Christoph Spahn, Mike Heilemann, Mia Conduit, Séamus Holden, Pedro Matos Pereira, Mariana Pinho]
documentation: https://github.com/HenriquesLab/DeepBacs/wiki
tags: [StarDist, segmentation, DeepBacs, 2D]
download_url: https://zenodo.org/record/5551009
covers:
- https://zenodo.org/api/iiif/v2/c6dca42a-62cd-423d-8216-b2ae20dab6a3:790a798b-26b2-425b-b1b6-9545d89daa7d:A_Segmentation_E.coli_large_FoV.png/full/750,/0/default.png
- type: dataset
id: Dataset_SplineDist_2D_DeepBacs
name: Escherichia coli bright field segmentation dataset - DeepBacs
description: Training and test images of live E. coli cells imaged under bright field for the task of segmentation.
cite:
- text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
doi: https://doi.org/10.1101/2021.11.03.467152
authors: [Christoph Spahn, Mike Heilemann]
documentation: https://github.com/HenriquesLab/DeepBacs/wiki
tags: [StarDist, segmentation, DeepBacs, 2D]
download_url: https://zenodo.org/record/5550935
covers:
- https://zenodo.org/api/iiif/v2/c6dca42a-62cd-423d-8216-b2ae20dab6a3:790a798b-26b2-425b-b1b6-9545d89daa7d:A_Segmentation_E.coli_large_FoV.png/full/750,/0/default.png
- type: dataset
id: Dataset_Noise2Void_2D_subtilis_DeepBacs
name: Bacillus subtilis denoising dataset - DeepBacs
description: Live-cell time series of vertically aligned B. subtilis cells expressing FtsZ-GFP protein fusion.
cite:
- text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
doi: https://doi.org/10.1101/2021.11.03.467152
authors: [Mia Conduit, Séamus Holden]
documentation: https://github.com/HenriquesLab/DeepBacs/wiki
tags: [Noise2Void, denoising, DeepBacs, 2D]
download_url: https://zenodo.org/record/5551135
covers:
- https://zenodo.org/api/iiif/v2/effa726a-dcdb-47a5-bd2e-63a4a6648ca7:aad7cd51-d389-4cf3-a84e-2d93d1891f20:B.subtilis_VerCINI_examples.png/full/750,/0/default.png
- type: dataset
id: Dataset_CARE_2D_coli_DeepBacs
name: Escherichia coli nucleoid denoising dataset - DeepBacs
description: Paired training and test images of H-NS-mScarlet-I expressing E. coli cells for image denoising.
cite:
- text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
doi: https://doi.org/10.1101/2021.11.03.467152
authors: [Christoph Spahn, Mike Heilemann]
documentation: https://github.com/HenriquesLab/DeepBacs/wiki
tags: [Noise2Void, CARE, denoising, DeepBacs, 2D]
download_url: https://zenodo.org/record/5551112
covers:
- https://zenodo.org/api/iiif/v2/0417c6d1-0fe3-4d48-bc5a-dfaee6398433:532d7c49-5961-4d61-8ba2-a040f2f19cfd:A_E.coli_H-NS_mScarlet_examples.png/full/750,/0/default.png
- type: dataset
id: Dataset_fnet_DeepBacs
name: Artificial labeling of E. coli membranes dataset - DeepBacs
description: Training and test images of E. coli cells for artificial labeling of membranes in brightfield images using fnet or CARE, as well as trained models for prediction of super-resolution membranes.
cite:
- text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
doi: https://doi.org/10.1101/2021.11.03.467152
authors: [Christoph Spahn, Mike Heilemann]
documentation: https://github.com/HenriquesLab/DeepBacs/wiki
tags: [fnet, CARE, artificial labelling, DeepBacs, 2D]
download_url: https://zenodo.org/record/5551123
covers:
- https://zenodo.org/api/iiif/v2/850bd89d-e7ca-4009-80ad-865819df1b6d:c18f2e19-3761-4852-8034-4cefcf7ca318:Artificial_labeling.png/full/750,/0/default.png
- type: dataset
id: Dataset_YOLOv2_coli_DeepBacs
name: Escherichia coli growth stage object detection dataset - DeepBacs
description: Training and test images of E. coli cells for object detection and classification.
cite:
- text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
doi: https://doi.org/10.1101/2021.11.03.467152
authors: [Christoph Spahn, Mike Heilemann]
documentation: https://github.com/HenriquesLab/DeepBacs/wiki
tags: [YOLOv2, object detection, DeepBacs, 2D]
download_url: https://zenodo.org/record/5551016
covers:
- https://zenodo.org/api/iiif/v2/e30f5766-cc61-4a7c-9d36-30681f2dcbc2:677b2046-5d0b-49df-b231-b5470b6fcbc8:A_Object_detection_cell_cycle.png/full/750,/0/default.png
- type: dataset
id: Dataset_YOLOv2_antibiotic_DeepBacs
name: Escherichia coli antibiotic phenotyping object detection dataset - DeepBacs
description: Training and test images of E. coli cells treated with different antibiotics for antibiotic phenotyping.
cite:
- text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
doi: https://doi.org/10.1101/2021.11.03.467152
authors: [Christoph Spahn, Mike Heilemann]
documentation: https://github.com/HenriquesLab/DeepBacs/wiki
tags: [YOLOv2, object detection, DeepBacs, 2D]
download_url: https://zenodo.org/record/5551057
covers:
- https://zenodo.org/api/iiif/v2/5b4a4304-ea44-47ce-9c75-4a069bfc9536:e3c27642-4605-42c3-ad3c-1e120917b97e:Antibiotic_profiling_examples.png/full/750,/0/default.png
#------------------------------------- Notebooks ---------------------------------------------
- type: application
id: Notebook Preview
source: https://raw.githubusercontent.com/bioimage-io/nbpreview/master/notebook-preview.imjoy.html
- type: application
id: Notebook_U-Net_2D_ZeroCostDL4Mic
name: U-Net (2D) - ZeroCostDL4Mic
description: 2D binary segmentation. U-Net is an encoder-decoder architecture originally used for image segmentation. The first half of the U-Net architecture is a downsampling convolutional neural network which acts as a feature extractor from input images. The other half upsamples these results and restores an image by combining results from downsampling with the upsampled images. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Olaf Ronneberger, Philipp Fischer, Thomas Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597. https://arxiv.org/abs/1505.04597"
doi: https://arxiv.org/abs/1505.04597
authors:
- Romain Laine and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/2D_Unet_notebook.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-Net_2D_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, U-Net, segmentation, ZeroCostDL4Mic, 2D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/U-Net_2D_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- Dataset_U-Net_2D_DeepBacs
- type: application
id: Notebook_U-Net_3D_ZeroCostDL4Mic
name: U-Net (3D) - ZeroCostDL4Mic
description: 3D binary segmentation. The 3D U-Net was first introduced by Çiçek et al for learning dense volumetric segmentations from sparsely annotated ground-truth data building upon the original U-Net architecture by Ronneberger et al. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, Olaf Ronneberger. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. https://arxiv.org/abs/1606.06650"
doi: https://arxiv.org/abs/1606.06650
authors:
- Daniel Krentzel and Estibaliz Gómez de Mariscal andthe ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/3D_Unet_notebook.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-Net_3D_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, U-Net, segmentation, ZeroCostDL4Mic, 3D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/U-Net_3D_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- type: application
id: Notebook_StarDist_2D_ZeroCostDL4Mic
name: StarDist (2D) - ZeroCostDL4Mic
description: 2D instance segmentation of oval objects (ie nuclei). StarDist is a deep-learning method that can be used to segment cell nuclei in 2D (xy) single images or in stacks (xyz). Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Uwe Schmidt, Martin Weigert, Coleman Broaddus, Gene Myers. Cell Detection with Star-Convex Polygons. MICCAI 2018 (2018). https://doi.org/10.1007/978-3-030-00934-2_30"
doi: https://doi.org/10.1007/978-3-030-00934-2_30
authors:
- Guillaume Jacquemet and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/2D_Stardist_notebook.png
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/StarDist_trainingTcells_trackmate.png
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/StarDist_trainingflo_trackmate.png
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/StarDist_trainingfluo_trackmate.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/StarDist_2D_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, StarDist, segmentation, ZeroCostDL4Mic, 2D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/StarDist_2D_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- Dataset_StarDist_2D_ZeroCostDL4Mic_2D
- Dataset_StarDist_Fluo_ZeroCostDL4Mic
- Dataset_StarDist_brightfield_ZeroCostDL4Mic
- Dataset_StarDist_brightfield2_ZeroCostDL4Mic
- Dataset_StarDist_fluo2_ZeroCostDL4Mic
- Dataset_StarDist_2D_DeepBacs
- type: application
id: Notebook_StarDist_3D_ZeroCostDL4Mic
name: StarDist (3D) - ZeroCostDL4Mic
description: 3D instance segmentation of oval objects (ie nuclei). StarDist is a deep-learning method that can be used to segment cell nuclei in 3D (xyz) images. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Martin Weigert, Uwe Schmidt, Robert Haase, Ko Sugawara, Gene Myers. Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy. arXiv. https://arxiv.org/abs/1908.03636"
doi: https://arxiv.org/abs/1908.03636
authors:
- Guillaume Jacquemet and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/StarDist_3D.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/StarDist_3D_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, StarDist, segmentation, ZeroCostDL4Mic, 3D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/StarDist_3D_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- type: application
id: Notebook_Noise2Void_2D_ZeroCostDL4Mic
name: Noise2Void (2D) - ZeroCostDL4Mic
description: self-supervised denoising of 2D images. Noise2Void 2D is deep-learning method that can be used to denoise 2D microscopy images. By running this notebook, you can train your own network and denoise your images. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "A. Krull, T. Buchholz and F. Jug, Noise2Void Learning Denoising From Single Noisy Images. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 2124-2132, https://doi.org/10.1109/CVPR.2019.00223."
doi: https://doi.org/10.1109/CVPR.2019.00223
authors:
- Romain Laine and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/N2V_2D_notebook.png
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/N2V2D_notebook_3.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Noise2Void_2D_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, Noise2Void, denoising, ZeroCostDL4Mic, 2D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Noise2Void_2D_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- Dataset_Noise2Void_2D_ZeroCostDL4Mic
- Dataset_Noise2Void_2D_subtilis_DeepBacs
- Dataset_Noisy_Nuclei_ZeroCostDL4Mic
- Dataset_CARE_2D_coli_DeepBacs
- type: application
id: Notebook_Noise2Void_3D_ZeroCostDL4Mic
name: Noise2VOID (3D) - ZeroCostDL4Mic
description: self-supervised denoising of 3D images. Noise2VOID 3D is deep-learning method that can be used to denoise 3D microscopy images. By running this notebook, you can train your own network and denoise your images. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "A. Krull, T. Buchholz and F. Jug. Noise2Void Learning Denoising From Single Noisy Images. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 2124-2132, https://doi.org/10.1109/CVPR.2019.00223."
doi: https://doi.org/10.1109/CVPR.2019.00223
authors:
- Romain Laine and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/N2V_3D_notebook.png
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/N2V_3D_notebook_2.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Noise2Void_3D_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, Noise2Void, denoising, ZeroCostDL4Mic, 3D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Noise2Void_3D_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- Dataset_Noise2Void_3D_ZeroCostDL4Mic
- type: application
id: Notebook_CARE_2D_ZeroCostDL4Mic
name: CARE (2D) - ZeroCostDL4Mic
description: Supervised restoration of 2D images. CARE is a neural network capable of image restoration from corrupted bio-images, first published in 2018 by Weigert et al. in Nature Methods. The network allows image denoising and resolution improvement in 2D and 3D images, in a supervised training manner. The function of the network is essentially determined by the set of images provided in the training dataset. For instance, if noisy images are provided as input and high signal-to-noise ratio images are provided as targets, the network will perform denoising. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Weigert, M., Schmidt, U., Boothe, T. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat Methods 15, 1090–1097 (2018). https://doi.org/10.1038/s41592-018-0216-7"
doi: https://doi.org/10.1038/s41592-018-0216-7
authors:
- Lucas von Chamier and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/CARE2D_notebook.png
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/CARE2D_notebook_2.png
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/CARE2D_notebook_3.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/CARE_2D_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, CARE, denoising, ZeroCostDL4Mic, 2D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/CARE_2D_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- Dataset_CARE_2D_ZeroCostDL4Mic
- Dataset_Noisy_Nuclei_ZeroCostDL4Mic
- Dataset_CARE_2D_coli_DeepBacs
- Dataset_fnet_DeepBacs
- type: application
id: Notebook_CARE_3D_ZeroCostDL4Mic
name: CARE (3D) - ZeroCostDL4Mic
description: Supervised restoration of 3D images. CARE is a neural network capable of image restoration from corrupted bio-images, first published in 2018 by Weigert et al. in Nature Methods. The network allows image denoising and resolution improvement in 2D and 3D images, in a supervised training manner. The function of the network is essentially determined by the set of images provided in the training dataset. For instance, if noisy images are provided as input and high signal-to-noise ratio images are provided as targets, the network will perform denoising. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Weigert, M., Schmidt, U., Boothe, T. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat Methods 15, 1090–1097 (2018). https://doi.org/10.1038/s41592-018-0216-7"
doi: https://doi.org/10.1038/s41592-018-0216-7
authors:
- Lucas von Chamier and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/3D_CARE_notebook.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/CARE_3D_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, CARE, denoising, ZeroCostDL4Mic, 3D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/CARE_3D_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- Dataset_CARE_3D_ZeroCostDL4Mic
- type: application
id: Notebook_fnet_3D_ZeroCostDL4Mic
name: Label-free Prediction - fnet - (3D) ZeroCostDL4Mic
description: Paired image-to-image translation of 3D images. Label-free Prediction (fnet) is a neural network used to infer the features of cellular structures from brightfield or EM images without coloured labels. The network is trained using paired training images from the same field of view, imaged in a label-free (e.g. brightfield) and labelled condition (e.g. fluorescent protein). When trained, this allows the user to identify certain structures from brightfield images alone. The performance of fnet may depend significantly on the structure at hand. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Ounkomol, C., Seshamani, S., Maleckar, M.M. et al. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat Methods 15, 917–920 (2018). https://doi.org/10.1038/s41592-018-0111-2"
doi: https://doi.org/10.1038/s41592-018-0111-2
authors:
- Lucas von Chamier and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/fnet_notebook.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/fnet_3D_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, fnet, labelling, ZeroCostDL4Mic, 3D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/fnet_3D_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- Dataset_fnet_3D_ZeroCostDL4Mic
- type: application
id: Notebook_fnet_2D_ZeroCostDL4Mic
name: Label-free Prediction - fnet - (2D) ZeroCostDL4Mic
description: Paired image-to-image translation of 2D images. Label-free Prediction (fnet) is a neural network used to infer the features of cellular structures from brightfield or EM images without coloured labels. The network is trained using paired training images from the same field of view, imaged in a label-free (e.g. brightfield) and labelled condition (e.g. fluorescent protein). When trained, this allows the user to identify certain structures from brightfield images alone. The performance of fnet may depend significantly on the structure at hand. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Ounkomol, C., Seshamani, S., Maleckar, M.M. et al. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat Methods 15, 917–920 (2018). https://doi.org/10.1038/s41592-018-0111-2"
doi: https://doi.org/10.1038/s41592-018-0111-2
authors:
- Lucas von Chamier and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/fnet_notebook.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/fnet_2D_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, fnet, labelling, ZeroCostDL4Mic, 2D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/fnet_2D_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- Dataset_fnet_DeepBacs
- type: application
id: Notebook_Deep-STORM_2D_ZeroCostDL4Mic
name: Deep-STORM (2D) - ZeroCostDL4Mic
description: Single Molecule Localization Microscopy (SMLM) image reconstruction from high-density emitter data. Deep-STORM is a neural network capable of image reconstruction from high-density single-molecule localization microscopy (SMLM), first published in 2018 by Nehme et al. in Optica. This network allows image reconstruction of 2D super-resolution images, in a supervised training manner. The network is trained using simulated high-density SMLM data for which the ground-truth is available. These simulations are obtained from random distribution of single molecules in a field-of-view and therefore do not imprint structural priors during training. The network output a super-resolution image with increased pixel density (typically upsampling factor of 8 in each dimension). Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Elias Nehme, Lucien E. Weiss, Tomer Michaeli, and Yoav Shechtman. Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica 5, 458-464 (2018)"
doi: https://doi.org/10.1364/OPTICA.5.000458
authors:
- Romain Laine and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/DeepSTORM_notebook.png
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/DeepSTORM_notebook_2.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Deep-STORM_2D_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, Deep-STORM, labelling, ZeroCostDL4Mic, 2D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Deep-STORM_2D_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- Dataset_Deep-STORM_ZeroCostDL4Mic
- type: application
id: Notebook_pix2pix_2D_ZeroCostDL4Mic
name: pix2pix (2D) - ZeroCostDL4Mic
description: Paired image-to-image translation of 2D images. pix2pix is a deep-learning method that can be used to translate one type of images into another. While pix2pix can potentially be used for any type of image-to-image translation, we demonstrate that it can be used to predict a fluorescent image from another fluorescent image. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros. Image-to-Image Translation with Conditional Adversarial Networks. arXiv:1611.07004."
doi: https://arxiv.org/abs/1611.07004
authors:
- Guillaume Jacquemet and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/pix2pix_notebook_2.png
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/pix2pix_notebook.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/pix2pix_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, pix2pix, ZeroCostDL4Mic, 2D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/pix2pix_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- Dataset_pix2pix_ZeroCostDL4Mic
- type: application
id: Notebook_CycleGAN_2D_ZeroCostDL4Mic
name: CycleGAN (2D) - ZeroCostDL4Mic
description: Unpaired image-to-image translation of 2D images. CycleGAN is a method that can capture the characteristics of one image domain and figure out how these characteristics could be translated into another image domain, all in the absence of any paired training examples (ie transform a horse into zebra or apples into oranges). While CycleGAN can potentially be used for any type of image-to-image translation, we illustrate that it can be used to predict what a fluorescent label would look like when imaged using another imaging modalities. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. arXiv:1703.10593"
doi: https://arxiv.org/abs/1703.10593
authors:
- Guillaume Jacquemet and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/cycleGAN_notebook.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/CycleGAN_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, CycleGAN, ZeroCostDL4Mic, 2D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/CycleGAN_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- Dataset_CycleGAN_ZeroCostDL4Mic
- type: application
id: Notebook_Augmentor_ZeroCostDL4Mic
name: Augmentor - ZeroCostDL4Mic
description: Artificially increase the size of your training dataset. Augmentor is a data augmentation library. Data augmentation can improve training progress by amplifying differences in the dataset. This can be useful if the available dataset is small since, in this case, it is possible that a network could quickly learn every example in the dataset (overfitting), without augmentation. Augmentation can be especially valuable when training dataset need to be manually labelled. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Marcus D Bloice, Peter M Roth, Andreas Holzinger, Biomedical image augmentation using Augmentor, Bioinformatics, Volume 35, Issue 21, 1 November 2019, Pages 4522–4524, https://doi.org/10.1093/bioinformatics/btz259"
doi: https://doi.org/10.1093/bioinformatics/btz259
authors:
- Guillaume Jacquemet and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/augmentor_notebook.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Tools/Augmentor_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, Augmentor, Data Augmentation, ZeroCostDL4Mic]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Tools/Augmentor_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- type: application
id: Notebook_DenoiSeg_2D_ZeroCostDL4Mic
name: DenoiSeg (2D) - ZeroCostDL4Mic
description: Joint denoising and binary segmentation of 2D images. DenoiSeg 2D is deep-learning method that can be used to jointly denoise and segment 2D microscopy images. The benefits of using DenoiSeg (compared to other Deep Learning-based segmentation methods) are more prononced when only a few annotated images are available. However, the denoising part requires many images to perform well. All the noisy images don't need to be labeled to train DenoiSeg. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Buchholz TO., Prakash M., Schmidt D., Krull A., Jug F. (2020) DenoiSeg: Joint Denoising and Segmentation. In: Bartoli A., Fusiello A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science, vol 12535. Springer, Cham. https://doi.org/10.1007/978-3-030-66415-2_21"
doi: https://doi.org/10.1007/978-3-030-66415-2_21
authors:
- Guillaume Jacquemet and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/Denoiseg_notebook.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Beta%20notebooks/DenoiSeg_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, CycleGAN, ZeroCostDL4Mic, 2D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Beta%20notebooks/DenoiSeg_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- type: application
id: Notebook_Deep-STORM_2D_ZeroCostDL4Mic_DeepImageJ
name: Deep-STORM (2D) - ZeroCostDL4Mic - DeepImageJ
description: Single Molecule Localization Microscopy (SMLM) image reconstruction from high-density emitter data. Deep-STORM is a neural network capable of image reconstruction from high-density single-molecule localization microscopy (SMLM), first published in 2018 by Nehme et al. in Optica. This network allows image reconstruction of 2D super-resolution images, in a supervised training manner. The network is trained using simulated high-density SMLM data for which the ground-truth is available. These simulations are obtained from random distribution of single molecules in a field-of-view and therefore do not imprint structural priors during training. The network output a super-resolution image with increased pixel density (typically upsampling factor of 8 in each dimension). Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these. Networks trained in this notebook can be used in Fiji via deepImageJ and ThunderSTORM plugin.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Elias Nehme, Lucien E. Weiss, Tomer Michaeli, and Yoav Shechtman. Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica 5, 458-464 (2018)"
doi: https://doi.org/10.1364/OPTICA.5.000458
authors:
- Estibaliz Gómez de Mariscal and the deepImageJ and the ZeroCostDL4Mic teams
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/DeepSTORM_notebook.png
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/DeepSTORM_notebook_2.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/BioImage.io%20notebooks/Deep-STORM_2D_ZeroCostDL4Mic_BioImageModelZoo_export.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, Deep-STORM, DeepImageJ, ZeroCostDL4Mic, 2D, deepImageJ]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/BioImage.io%20notebooks/Deep-STORM_2D_ZeroCostDL4Mic_BioImageModelZoo_export.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- Dataset_Deep-STORM_ZeroCostDL4Mic
- type: application
id: Notebook_U-Net_2D_multilabel_ZeroCostDL4Mic
name: U-Net (2D) multilabel segmentation - ZeroCostDL4Mic
description: 2D semantic segmentation. U-Net is an encoder-decoder architecture originally used for image segmentation. The first half of the U-Net architecture is a downsampling convolutional neural network which acts as a feature extractor from input images. The other half upsamples these results and restores an image by combining results from downsampling with the upsampled images. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Olaf Ronneberger, Philipp Fischer, Thomas Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597. https://arxiv.org/abs/1505.04597"
doi: https://arxiv.org/abs/1505.04597
authors:
- Estibaliz Gómez de Mariscal and the ZeroCostDL4Mic team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/UNet_Multilabel_example.png
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/UNet-multilabel-example-zoom.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-Net_2D_Multilabel_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, u-net, segmentation, semantic-segmentation, multilabel, ZeroCostDL4Mic, 2D]
download_url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-Net_2D_Multilabel_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- Dataset_U-Net_2D_multilabel_DeepBacs
- type: application
id: Notebook_RCAN_3D_ZeroCostDL4Mic
name: RCAN (3D) - ZeroCostDL4Mic
description: Supervised restoration of 3D images. RCAN is a neural network capable of image restoration from corrupted bio-images. The network allows image denoising and resolution improvement in 3D images, in a supervised training manner. The function of the network is essentially determined by the set of images provided in the training dataset. For instance, if noisy images are provided as input and high signal-to-noise ratio images are provided as targets, the network will perform denoising. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Chen, J., Sasaki, H., Lai, H. et al. Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes. Nat Methods 18, 678–687 (2021). https://doi.org/10.1038/s41592-021-01155-x"
doi: https://doi.org/10.1038/s41592-021-01155-x
authors:
- Guillaume Jacquemet and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/3D_CARE_notebook.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Beta%20notebooks/3D-RCAN_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
tags: [colab, notebook, 3D-RCAN, denoising, ZeroCostDL4Mic, 3D]
download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Beta%20notebooks/3D-RCAN_ZeroCostDL4Mic.ipynb
git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
links:
- Notebook Preview
- Dataset_CARE_3D_ZeroCostDL4Mic
- type: application
id: Notebook_SplineDist_2D_ZeroCostDL4Mic
name: SplineDist (2D) - ZeroCostDL4Mic
description: Instance segmentation of 2D images. SplineDist is a neural network inspired by StarDist, capable of performing image instance segmentation. Unlike StarDist, SplineDist uses cubic splines to describe the contour of each object and therefore can potentially segment objects of any shapes. This version is only for 2D dataset. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
cite:
- text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
doi: https://doi.org/10.1038/s41467-021-22518-0
- text: "Soham Mandal, Virginie Uhlmann. SplineDist: Automated Cell Segmentation With Spline Curves. bioRxiv 2020.10.27.357640; doi: https://doi.org/10.1101/2020.10.27.357640"
doi: https://doi.org/10.1101/2020.10.27.357640
authors:
- Romain F. Laine and the ZeroCostDL4Mic Team
covers:
- https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/SplineDist_overlay_cropped.png
badges:
- label: Open in Colab
icon: https://colab.research.google.com/assets/colab-badge.svg
url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Beta%20notebooks/SplineDist_2D_ZeroCostDL4Mic.ipynb
documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md