BANIS: Baseline for Affinity-based Neuron Instance Segmentation

An easily adaptable baseline for the Neuron Instance Segmentation Benchmark (NISB), predicting affinities with modern architectures and simple connected components for post-processing

Prerequisites

Download NISB datasets and set up a conda/mamba environment:

# With environment.yaml
mamba env create -f environment.yaml
mamba activate nisb

# Without yaml
mamba create -n nisb -c conda-forge python=3.11 -y
mamba activate nisb 
pip install torch torchvision torchaudio numpy connected-components-3d numba pytorch-lightning zarr monai scipy cython tensorboard
pip install -e git+https://github.com/MIC-DKFZ/MedNeXt.git#egg=mednextv1
pip install git+https://github.com/funkelab/funlib.evaluate.git 

Tested on a Slurm cluster with nodes equipped with 1 NVIDIA A40 GPU and 500 GB RAM (stay tuned for a less RAM-intensive version).

Usage

Run a single training session (BANIS-S(mall)):

python BANIS.py --seed 0 --batch_size 8 --n_steps 50000 --data_setting base --base_data_path /local/dataset/dir/ --save_path /local/logging/dir/

Results are logged to TensorBoard. For GPUs with less than 48 GB memory, reduce batch_size (and adjust n_steps / learning_rate). For BANIS-L(arge) add --model_id L --kernel_size 5. Additional options are in parse_args of BANIS.py.

To run multiple jobs on Slurm, adjust config.yaml and start_run.sh, then:

python slurm_job_scheduler.py

Evaluation

To evaluate a predicted segmentation (.zarr or .npy):

python metrics.py --pred_seg /path/to/predictions.zarr --skel_path /path/to/skeleton.pkl [--load_to_memory]

Visualization

To visualize the validation cube of each dataset, run:

 show_data.py --base_path /local/benchmark/dir/