CellSAM

This is the official repository for CellSAM: Segment Anything in Microscopy Images of C. elegans.

Installation

Create a virtual environment conda create -n cellsam python=3.10 -y and activate it conda activate cellsam
Install Pytorch 2.0
git clone https://github.com/DevoLearn/CellSAM.git
Enter the CellSAM folder cd CellSAM and run pip install -e .

Get Started

Download the model checkpoint and place it at e.g., work_dir/CellSAM/cellsam_vit_b

We provide three ways to quickly test the model on your microscopy images:

Command line

python CellSAM_Inference.py # segment the demo image

Segment other images with the following flags:

-i input_img
-o output path
--box bounding box of the segmentation target

Jupyter-notebook

We provide a step-by-step tutorial on CoLab

You can also run it locally with tutorial_quickstart.ipynb.

GUI

Install PyQt5 with pip: pip install PyQt5 or conda: conda install -c anaconda pyqt

python gui.py

Model Training

We have trained only mask decoder by freezing the image encode ,prompt encoder .

Training on multiple GPUs (Recommend)

The model was trained on one v100 gpu .

sbatch train_multi_gpus.sh

When the training process is done, please convert the checkpoint to SAM's format for convenient inference.

python utils/ckpt_convert.py # Please set the corresponding checkpoint path first

Training on one GPU

python train_one_gpu.py

Data preprocessing

Download the demo dataset and unzip it to data/CellTrain/.

This dataset contains microscopy images of C. elegans. The names of the cell label are available at CellTrackingCHALLENGE.

Split dataset: 80% for training and 20% for testing
Max-min normalization
Resample image size to 1024x2014
Save the pre-processed images and labels as png files

One can navigate through th folder for the notebooks/explore_preprocess_cell_tracking_challeneg_dataset.ipynb for converting the 3d tiff files to microscopy images to png images

Acknowledgements

We highly appreciate all the Google summer of code organizers and dataset owners for providing the public dataset to the community.
Thanks for the bracly alicea, mayukh deb and mainak deb through entire GSOC period
We thank Meta AI for making the source code of segment anything publicly available.
We also thank Alexandre Bonnet for sharing this insightful blog.
we also thanks for the code MedSAM Code owner for there code on Medical images MedSAM

Final Report

Please find the final report for this project.

aiyou9 / CellSAM