Segger

Segger is a cell segmentation model for single-molecule resolved datasets, addressing the challenges of accurate and fast single-cell segmentation in imaging-based spatial omics. By leveraging the co-occurrence of nucleic and cytoplasmic molecules (e.g., transcripts), Segger employs a heterogeneous graph structure integrating fixed-radius nearest neighbor graphs for nuclei and molecules, with edges connecting transcripts to nuclei based on spatial proximity. A graph neural network (GNN) propagates information across these edges to learn molecule-nuclei associations, refining cell borders post-training. Benchmarks on 10X Xenium and MERSCOPE demonstrate Segger's superior accuracy and efficiency over existing methods like Baysor and Cellpose, with faster training and easy adaptability to different datasets and technologies.

Installation

To install Segger, clone this repository and install the required dependencies:

git clone https://github.com/EliHei2/segger_dev.git
cd segger_dev
pip install -r requirements.txt

Alternatively, you can create a conda environment using the provided environment.yml file:

conda env create -f environment.yml
conda activate segger

Download Pancreas Dataset

Download the Pancreas dataset from 10x Genomics:

1. Go to the Xenium Human Pancreatic Dataset Explorer.
2. Download the transcripts.csv.gz and nucleus_boundaries.csv.gz files.
3. Place these files in a directory, e.g., data_raw/pancreas.

Creating Dataset

To create a dataset for Segger, use the create_data.py script. The script takes several arguments to customize the dataset creation process.

python create_data.py --transcripts_path data_raw/pancreas/transcripts.csv.gz --nuclei_path data_raw/pancreas/nucleus_boundaries.csv.gz --output_dir data_tidy/pyg_datasets/pancreas --d_x 180 --d_y 180 --x_size 200 --y_size 200 --r 3 --val_prob 0.1 --test_prob 0.1 --k_nc 3 --dist_nc 10 --k_tx 5 --dist_tx 3 --compute_labels True --sampling_rate 1

This command will process the Pancreas dataset and save the processed data in the specified output directory.

Training

To train the Segger model, use the train.py script. The script takes several arguments to customize the training process.

python train.py --train_dir data_tidy/pyg_datasets/pancreas/train_tiles/processed --val_dir data_tidy/pyg_datasets/pancreas/val_tiles/processed --test_dir data_tidy/pyg_datasets/pancreas/test_tiles/processed --epochs 100 --batch_size_train 4 --batch_size_val 4 --learning_rate 1e-3 --init_emb 8 --hidden_channels 64 --out_channels 16 --heads 4 --aggr sum --accelerator cuda --strategy auto --precision 16-mixed --devices 4 --default_root_dir ./models/pancreas

This command will train the Segger model on the processed Pancreas dataset and save the trained model in the specified output directory.

Prediction

To make predictions using a trained Segger model, use the predict.py script. The script takes several arguments to customize the prediction process.

python predict.py --train_dir data_tidy/pyg_datasets/pancreas/train_tiles/processed --val_dir data_tidy/pyg_datasets/pancreas/val_tiles/processed --test_dir data_tidy/pyg_datasets/pancreas/test_tiles/processed --checkpoint_path ./models/pancreas/lightning_logs/version_0/checkpoints/epoch=99-step=100.ckpt --batch_size 1 --init_emb 8 --hidden_channels 64 --out_channels 16 --heads 4 --aggr sum --accelerator cuda --devices 1 --default_root_dir ./log_final --score_cut 0.5 --k_nc 4 --dist_nc 20 --k_tx 5 --dist_tx 10

This command will use the trained Segger model to make predictions on the Pancreas dataset and save the predictions in the specified output directory.

Benchmarking

Benchmarking utilities are provided to evaluate the performance of the Segger model. You can find these utilities in the benchmark directory.

Visualization

Visualization scripts are also provided to help visualize the results. You can find these scripts in the benchmark directory.

License

This project is licensed under the MIT License - see the LICENSE file for details.