manifest.bioimageio_dij_install_model.yaml

format_version: 0.2.2
type: collection
name: deepImageJ Collection
tags: [deepimagej, bioimage.io]
description: "Resources for BioImgage.IO curated by the deepImageJ team."
authors: []
documentation: https://deepimagej.github.io/deepimagej/
maintainers: []

cite:
  - text: 'E. Gómez-de-Mariscal, C. García-López-de-Haro, W. Ouyang, L. Donati, E. Lundberg, M. Unser, A. Muñoz-Barrutia, D. Sage, "DeepImageJ: A user-friendly environment to run deep learning models in ImageJ", Nat Methods 18, 1192–1195 (2021).'
    doi: 10.1038/s41592-021-01262-9

id: deepimagej
config:
  id: deepimagej
  name: DeepImageJ
  tags:
    - deepimagej
  logo: https://raw.githubusercontent.com/deepimagej/models/master/logos/logo.png
  icon: https://raw.githubusercontent.com/deepimagej/models/master/logos/icon.png
  splash_title: deepImageJ
  splash_subtitle: "A user-friendly plugin to run deep learning models in ImageJ"
  splash_feature_list:
  explore_button_text: "Start Exploring"
  background_image: "static/img/zoo-background.svg"
  resource_types:
    - model
    - notebook
    - application
  url_root: https://raw.githubusercontent.com/deepimagej/models/master

collection:
# ==================applications====================
  - id: deepimagej
    name: deepImageJ
    description: "DeepImageJ is a user-friendly plugin that enables the use of pre-trained deep learning models in ImageJ and Fiji."
    type: application
    cite:
      - text: "Gómez-de-Mariscal, E., García-López-de-Haro, C., Ouyang, W., Donati, L., Lundberg E., Unser, M., Muñoz-Barrutia, A. and Sage, D. DeepImageJ: A user-friendly plugin to run deep learning models in ImageJ, BioRxiv, 2019"
        doi: https://doi.org/10.1101/799270
    authors:
      - name: deepImageJ
        affiliation: EPFL, UC3M
    source: https://raw.githubusercontent.com/deepimagej/models/master/src/deepimagej-app.imjoy.html
    tags: [software, bioengine, deepimagej]
    icon: https://raw.githubusercontent.com/deepimagej/models/master/logos/icon.png
    documentation: https://deepimagej.github.io/
    git_repo: https://github.com/deepimagej/deepimagej-plugin
    config:
      supported_weight_formats: ["tensorflow_saved_model_bundle", "torchscript"]
      
  - id: unet-pancreaticcellsegmentation
    type: application
    name: 2D U-Net for binary segmentation
    description: Easy example to define a 2D U-Net for segmentation with Keras and import it into DeepImageJ format
    cite:
      - text: "Falk, T., Mai, D., Bensch, R. et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat Methods 16, 67–70 (2019)."
        doi: https://doi.org/10.1038/s41592-018-0261-2
    authors:
      - name: Ignacio Arganda-Carreras
        affiliation: EPFL, UC3M
      - name: DeepImageJ
        affiliation: EPFL, UC3M
    covers:
      - https://raw.githubusercontent.com/deepimagej/models/master/u-net_pancreatic_segmentation/notebook_intro.png
      - https://raw.githubusercontent.com/deepimagej/models/master/u-net_pancreatic_segmentation/usecase.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/deepimagej/models/blob/master/u-net_pancreatic_segmentation/U_Net_PhC_C2DL_PSC_segmentation.ipynb
    documentation: https://github.com/miura/NEUBIAS_AnalystSchool2020/tree/master/Ignacio
    tags:
      - unet
      - segmentation
      - deepimagej
      - notebook
      - training
      - cell-segmentation
    source: https://raw.githubusercontent.com/deepimagej/models/master/u-net_pancreatic_segmentation/U_Net_PhC_C2DL_PSC_segmentation.ipynb
    links:
      - UNet2DPancreaticSegmentation
  - id: smlm-deepimagej
    type: application
    name: SMLM-superresolution
    description: Single molecule localization microscopy (SMLM) processing using deepImageJ and ThunderSTORM in an ImageJ macro.
    covers:
      - https://raw.githubusercontent.com/deepimagej/models/master/workflows/smlm_deepstorm/cover.png
    download_url: https://raw.githubusercontent.com/deepimagej/imagej-macros/master/DeepSTORM4stacksThunderSTORM.ijm
    source: https://raw.githubusercontent.com/deepimagej/imagej-macros/master/DeepSTORM4stacksThunderSTORM.ijm
    cite:
      - text: Lucas von Chamier et al., Nature Communications 2021
        doi: https://doi.org/10.1038/s41467-021-22518-0
      - text: Gómez de Mariscal et al. bioRxiv 2019
        doi: https://doi.org/10.1101/799270
      - text: M. Ovesný, et al., Bioinformatics 2014
        doi: https://doi.org/10.1093/bioinformatics/btu202
    authors: 
      - name: DeepImageJ
        affiliation: EPFL, UC3M
    icon: https://raw.githubusercontent.com/deepimagej/models/master/logos/icon.png
    documentation: https://raw.githubusercontent.com/deepimagej/models/master/workflows/smlm_deepstorm/README.md
    tags:
      - deepimagej
      - smlm
      - macro
      - deepstorm
      - thunderstorm
      - workflow
      - pipeline
      - superresolution
  - id: EVsTEMsegmentationFRUNet
    type: application
    name: Small extracellular vesicle instance segmentation (FRU-Net)
    description: Ready to use notebook for the segmentation of small extrcaellular vesicles in transmission electron microscopy (TEM) images. The notebook is optimized to use it in Google Colaboratory. It will download the original code and dataset, and make the inference connecting with Google's GPU.
    cite:
      - text: "Gómez-de-Mariscal, E. et al., Deep-Learning-Based Segmentation of SmallExtracellular Vesicles in Transmission Electron Microscopy Images Scientific Reports, (2019)"
        doi: https://doi.org/10.1038/s41598-019-49431-3
    authors:
      - name: Estibaliz Gómez-de-Mariscal
        affiliation: Universidad Carlos III de Madrid
      - name: Martin Maška
        affiliation: Masaryk University
      - name: Anna Kotrbová
        affiliation: Masaryk University
      - name: Vendula Pospíchalová
        affiliation: Masaryk University
      - name: Pavel Matula
        affiliation: Masaryk University
      - name: Arrate Muñoz-Barrutia
        affiliation: Universidad Carlos III de Madrid
    covers:
      - https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41598-019-49431-3/MediaObjects/41598_2019_49431_Fig1_HTML.png
      - https://raw.githubusercontent.com/deepimagej/models/master/fru-net_sev_segmentation/frunet_sev.jpg
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/BIIG-UC3M/FRU-Net-TEM-segmentation/blob/main/FRUnet_TEM_Exosomes_sEV.ipynb
    documentation: https://raw.githubusercontent.com/deepimagej/models/master/fru-net_sev_segmentation/README.md
    tags:
      - extracellular-vesicles
      - segmentation
      - TEM
      - notebook
      - model-inference
      - google-colab
      - workflow
      - pipeline
    source: https://raw.githubusercontent.com/BIIG-UC3M/FRU-Net-TEM-segmentation/master/FRUnet_TEM_Exosomes_sEV.ipynb
    links:
      - FRUNet2DsEVSegmentation

# ==================datasets====================
  - id: MoNuSeg_digital_pathology_miccai2018
    type: dataset
    name: Multi-Organ Nucleus Segmentation Challenge - MICCAI 2018
    description: Labelled images for instance segmentation of cell nuclei in digital pathology datasets (MoNuSeg 2018 Challenge).
    cite:
      - text: "Neeraj Kumar et al. Transactions on medical imaging 2020"
        doi: https://doi.org/10.1109/TMI.2019.2947628
    authors: 
      - name: DeepImageJ
        affiliation: EPFL, UC3M
    documentation: https://monuseg.grand-challenge.org/
    tags: [StarDist, segmentation, pathology, H&E, histology, 2D, nuclei segmentation, digital pathology]
    source: https://monuseg.grand-challenge.org/Data/
    covers:
      - https://raw.githubusercontent.com/deepimagej/models/master/datasets/MoNuSeg1.jpg
      - https://raw.githubusercontent.com/deepimagej/models/master/datasets/MoNuSeg2.jpg
      - https://raw.githubusercontent.com/deepimagej/models/master/datasets/MoNuSeg3.jpg

# ==================models====================
  - id: FRUNet2DsEVSegmentation
    type: model
    rdf_source: fru-net_sev_segmentation/model.yaml
    links:
      - deepimagej
      - EVsTEMsegmentationFRUNet
      - imjoy/BioImageIO-Packager
    download_url: https://zenodo.org/record/6559475/files/deepimagej_fru-net_sev_segmentation.zip

  - id: UNet2DHeLaSegmentation
    type: model
    rdf_source: u-net_hela_segmentation/model.yaml
    links:
      - deepimagej
      - imjoy/BioImageIO-Packager
    download_url: https://zenodo.org/record/6827059/files/deepimagej_hela_unet_segmentation.zip

  - id: UNet2DPancreaticSegmentation
    type: model
    rdf_source: u-net_pancreatic_segmentation/rdf.yaml
    download_url: https://zenodo.org/record/6514622/files/unet_pancreatic_bioimageio.zip
    links:
      - deepimagej
      - unet-pancreaticcellsegmentation
      - imjoy/BioImageIO-Packager

  - id: UNet2DGlioblastomaSegmentation
    type: model
    rdf_source: u-net_glioblastoma_segmentation/model.yaml
    download_url: https://zenodo.org/record/4155785/files/deepimagej_u-net_glioblastoma_segmentation.zip
    links:
      - deepimagej
      - imjoy/BioImageIO-Packager

  - id: SMLMDensityMapEstimationDEFCoN
    type: model
    rdf_source: defcon_density_map_estimation/model.yaml
    download_url: https://zenodo.org/record/4608442/files/SMLM_Density%20Map_Estimation_%28DEFCoN%29.bioimage.io.model.zip
    links:
      - deepimagej
      - imjoy/BioImageIO-Packager

  - id: DeepSTORMZeroCostDL4Mic
    type: model
    rdf_source: deepstorm_zerocostdl4mic/model.yaml
    download_url: https://sandbox.zenodo.org/record/907832/files/bioimage.io.model.zip
    links:
      - zero/Notebook_Deep-STORM_2D_ZeroCostDL4Mic_DeepImageJ
      - deepimagej
      - imjoy/BioImageIO-Packager

  - id: 2DUNetZeroCostDL4Mic
    type: model
    rdf_source: 2du-net_zerocostdl4mic/model.yaml
    download_url: https://sandbox.zenodo.org/record/904905/files/DeepImageJ_2D_UNet_ZeroCostDL4Mic.zip
    links:
      - zero/Notebook_U-Net_2D_ZeroCostDL4Mic_DeepImageJ
      - deepimagej
      - imjoy/BioImageIO-Packager

  - id: MU-Lux_CTC_PhC-C2DL-PSC
    type: model
    rdf_source: mu-lux_ctc_phc-c2dl-psc/model.yaml
    download_url: https://sandbox.zenodo.org/record/914413/files/mu-lux_ctc_phc-c2dl-psc.zip
    links:
      - deepimagej
      - imjoy/BioImageIO-Packager

  - id: WidefieldTxredSuperResolution
    type: model
    rdf_source: widefield_txred_super-resolution/model.yaml
    download_url: https://sandbox.zenodo.org/record/913992/files/widefield_txred_super-resolution.zip
    links:
      - deepimagej
      - imjoy/BioImageIO-Packager

  - id: WidefieldDapiSuperResolution
    type: model
    rdf_source: widefield_dapi_super-resolution/model.yaml
    download_url: https://sandbox.zenodo.org/record/914422/files/widefield_dapi_super-resolution.zip
    links:
      - deepimagej
      - imjoy/BioImageIO-Packager

  - id: WidefieldFitcSuperResolution
    type: model
    rdf_source: widefield_fitc_super-resolution/model.yaml
    download_url: https://sandbox.zenodo.org/record/914425/files/widefield_fitc_super-resolution.zip
    links:
      - deepimagej
      - imjoy/BioImageIO-Packager

  - id: JonesVirtualStaining
    type: model
    rdf_source: jones_virtual_staining/model.yaml
    download_url: https://zenodo.org/record/7260869/files/jones_bioimageio.zip
    links:
      - deepimagej
      - imjoy/BioImageIO-Packager

  - id: Mt3VirtualStaining
    type: model
    rdf_source: mt3_virtual_staining/model.yaml
    download_url: https://sandbox.zenodo.org/record/906805/files/bioimage.io.model.zip
    links:
      - deepimagej
      - imjoy/BioImageIO-Packager

  - id: Usiigaci
    type: model
    rdf_source: usiigaci/model.yaml
    download_url: https://zenodo.org/record/4155785/files/usiigaci.bioimage.io.model.zip
    links:
      - deepimagej
      - imjoy/BioImageIO-Packager

  - id: SkinLesionClassification
    type: model
    rdf_source: skin_lesion_classification/model.yaml
    download_url: https://sandbox.zenodo.org/record/906807/files/bioimage.io.model.zip
    links:
      - deepimagej

  - id: 3DUNetZeroCostDL4Mic
    type: model
    rdf_source: 3du-net_zerocostdl4mic/model.yaml
    download_url: https://sandbox.zenodo.org/record/907831/files/3DUNet_ZeroCostDL4Mic.bioimage.io.model.zip
    links:
      - deepimagej
      - zero/Notebook_U-Net_3D_ZeroCostDL4Mic_DeepImageJ
      - imjoy/BioImageIO-Packager

  - id: Stardist2DZeroCostDL4Mic
    type: model
    rdf_source: stardist2D_zerocostdl4mic/model.yaml
    download_url: https://sandbox.zenodo.org/record/894493/files/MoNu_HE_StarDist_9JUL.bioimage.io.model.zip
    links:
      - deepimagej
      - zero/Notebook_StarDist_2D_ZeroCostDL4Mic_DeepImageJ
      - MoNuSeg_digital_pathology_miccai2018