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https://github.com/bowang-lab/unicell

Universal cellular segmentation models

https://github.com/bowang-lab/unicell

Science Score: 26.0%

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    Low similarity (13.3%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

Universal cellular segmentation models

Basic Info
  • Host: GitHub
  • Owner: bowang-lab
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 53 MB
Statistics
  • Stars: 3
  • Watchers: 3
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 4 years ago · Last pushed over 3 years ago
Metadata Files
Readme License

README.md

UniCell

UniCell is a universal cellular segmentation tool for multi-modality microscopy images. It has three main features

  • It works for various microscopy images, especially for RGB images, e.g., bone marrow slides. We also provide a new annotated RGB cell image dataset (download), which is complementary to existing cell segmentation datasets.
  • It works for various image formats (e.g., png, bmp, jpg, tif, tiff) without format converting and does not require users to manually select segmentation models and image channels.
  • The inference speed is fast (~0.07s for 256x256 image and ~0.33s for 512x512 image on NVIDAI 2080Ti).

Installation

bash conda create -n unicell python=3.9 -y git clone https://github.com/bowang-lab/unicell.git cd unicell pip install -e .

Train UniCell

bash unicell_train -dir <path to training set> --model_folder <unicell> --batch_size 32

Training set folder structure

bash training_set/ |----images |--------img1.png |--------img2.jpg |--------img3.bmp |--------img4.tif |--------img5.tiff |----labels |--------img1_label.tiff |--------img2_label.tiff |--------img3_label.tiff |--------img4_label.tiff |--------img5_label.tiff

UniCell does not have limitation on the image format. The corresponding labels should have a suffix _label.tiff.

Inference

bash unicell_predict -i <input path> -o <output path> --pretrain_model unicell --contour_overlay

Compute metrics

We provide a interface to compute various metrics for cell segmentation results, including F1 score, precision, recall, the number of missing cells, the number of false-positive cells, and dice

bash com_metric -g <path to ground truth folder> -s <path to segmentation folder> -thre 0.5 0.7 0.9 -o <path to save folder> -n <csv name>

Graphical User Interface (GUI)

We develop a GUI plugin based on napari, which enables users who may not have coding experience to analyze their microscopy images visually in real time.

Install GUI: pip install napari-unicell

napari-gui

Online demo

We deploy an online demo on huggingface, which enables users to directly upload the cell images to get the segmentation results.

Remark: huggingface provides 2 free CPU for the deployment. So the inference can only use CPU, which is a little bit slow for large images (e.g., 1000x1000). We recommend using the command line interface or GUI to analyze large images if GPU is available on your local desktop.

huggingface

Owner

  • Name: WangLab @ U of T
  • Login: bowang-lab
  • Kind: organization
  • Location: 190 Elizabeth St, Toronto, ON M5G 2C4 Canada

BoWang's Lab at University of Toronto

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Last synced: about 1 year ago

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  • Total Commits: 14
  • Total Committers: 1
  • Avg Commits per committer: 14.0
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  • Commits: 0
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  • Development Distribution Score (DDS): 0.0
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Jun 1****4@q****m 14
Committer Domains (Top 20 + Academic)
qq.com: 1

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Last synced: 12 months ago

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  • Total issues: 0
  • Total pull requests: 0
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  • Average comments per issue: 0
  • Average comments per pull request: 0
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  • Bot pull requests: 0
Past Year
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  • Pull requests: 0
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 11 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
pypi.org: unicell

Universal cell segmentation

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 11 Last month
Rankings
Dependent packages count: 6.6%
Average: 18.6%
Dependent repos count: 30.6%
Maintainers (1)
junma11
Last synced: 11 months ago

Dependencies

setup.py pypi
  • einops *
  • gdown *
  • imagecodecs *
  • matplotlib *
  • monai *
  • numba *
  • numpy *
  • pandas *
  • pillow *
  • psutil *
  • scikit-image *
  • scipy *
  • tensorboard *
  • torchvision *
  • tqdm *