Train a deep learning model for cell segmentation in a few minutes from scratch.
This is a QuPath extension for Cellsparse API.
This is a part of the following paper. Please cite it when you use this project.
- Sugawara, K. Training deep learning models for cell image segmentation with sparse annotations. bioRxiv 2023. doi:10.1101/2023.06.13.544786
Drag and drop the extension file to QuPath and restart it.
Set up the server following the instructions in the link below.
https://github.com/ksugar/cellsparse-api
If you use SAM API together with this API, you need to use different ports for them.
For example, the following command will launch Cellsparse API on the port 8000 (default).
(cellsparse-api)$ uvicorn cellsparse_api.main:app
INFO: Started server process [26240]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)Then, you can launch SAM API on the port 18000 as follows.
(samapi) samapi.main:app --port 18000
INFO: Started server process [12060]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://127.0.0.1:18000 (Press CTRL+C to quit)On QuPath, set the server URL for SAM (Extensions > SAM > Server URL) to http://localhost:18000/sam/.
Currently, StarDist, Cellpose and ELEPHANT are available.
Train a model with the annotations in the current image.
In the training, foreground and background annotations need to be assigned to the annotation classes with the specific names, Foreground and Background, respectively. These names are case-sensitive.
Run inference with the latest model.
Reset a model (randomly initialized).
Set the server URL for Cellsparse API.
Please cite my paper on bioRxiv.
@article {Sugawara2023.06.13.544786,
author = {Ko Sugawara},
title = {Training deep learning models for cell image segmentation with sparse annotations},
elocation-id = {2023.06.13.544786},
year = {2023},
doi = {10.1101/2023.06.13.544786},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Deep learning is becoming more prominent in cell image analysis. However, collecting the annotated data required to train efficient deep-learning models remains a major obstacle. I demonstrate that functional performance can be achieved even with sparsely annotated data. Furthermore, I show that the selection of sparse cell annotations significantly impacts performance. I modified Cellpose and StarDist to enable training with sparsely annotated data and evaluated them in conjunction with ELEPHANT, a cell tracking algorithm that internally uses U-Net based cell segmentation. These results illustrate that sparse annotation is a generally effective strategy in deep learning-based cell image segmentation. Finally, I demonstrate that with the help of the Segment Anything Model (SAM), it is feasible to build an effective deep learning model of cell image segmentation from scratch just in a few minutes.Competing Interest StatementKS is employed part-time by LPIXEL Inc.},
URL = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.13.544786},
eprint = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.13.544786.full.pdf},
journal = {bioRxiv}
}