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Deepgrow Examples

This folder contains examples to run train and validate a deepgrow 2D/3D model. It also has notebooks to run inference over trained model.

implementation based on:

Sakinis et al., Interactive segmentation of medical images through fully convolutional neural networks. (2019) https://arxiv.org/abs/1903.08205

1. Data

Training a deepgrow model requires data. Some public available datasets which are used in the examples can be downloaded from Medical Segmentation Decathlon or Synapse.

2. Questions and bugs

• For questions relating to the use of MONAI, please us our Discussions tab on the main repository of MONAI.
• For bugs relating to MONAI functionality, please create an issue on the main repository.
• For bugs relating to the running of a tutorial, please create an issue in this repository.

3. List of notebooks and examples

Prepare Your Data

This example is a standard PyTorch program and helps user to prepare training input for 2D or 3D.

# Run to know all possible options
python ./prepare_dataset.py -h

# Prepare dataset to train a 2D Deepgrow model
python ./prepare_dataset.py
    --dimensions   2 \
    --dataset_root MSD_Task09_Spleen \
    --dataset_json MSD_Task09_Spleen/dataset.json \
    --output       deepgrow/2D/MSD_Task09_Spleen

# Prepare dataset to train a 3D Deepgrow model
python ./prepare_dataset.py
    --dimensions   3 \
    --dataset_root MSD_Task09_Spleen \
    --dataset_json MSD_Task09_Spleen/dataset.json \
    --output       deepgrow/3D/MSD_Task09_Spleen

Deepgrow 2D Training

This example is a standard PyTorch program and helps user to run training over pre-processed dataset for 2D.

# Run to know all possible options
python ./train.py -h

# Train a 2D Deepgrow model on single-gpu
python ./train.py
    --input       deepgrow/2D/MSD_Task09_Spleen/dataset.json \
    --output      models/2D \
    --epochs      50

# Train a 2D Deepgrow model on multi-gpu (NVIDIA)
python -m torch.distributed.launch \
    --nproc_per_node=`nvidia-smi -L | wc -l` \
    --nnodes=1 \
    --node_rank=0 \
    --master_addr="localhost" \
    --master_port=1234 \
    -m train \
    --multi_gpu true \
    --input       deepgrow/2D/MSD_Task09_Spleen/dataset.json \
    --output      models/2D \
    --epochs      50

# After training to export/save as torch script model
python ./train.py
    --input       models/2D/model.pt \
    --output      models/2D/model.ts \
    --export      true

Deepgrow 2D Validation

This example is a standard PyTorch program and helps user to run evaluation for a trained 2D model.

# Run to know all possible options
python ./validate.py -h

# Evaluate a 2D Deepgrow model
python ./validate.py
    --input       deepgrow/2D/MSD_Task09_Spleen/dataset.json \
    --output      eval/2D \
    --model_path  models/2D/model.pt

Deepgrow 2D Inference

This notebook helps to run pre-transforms before running inference over a Deepgrow 2D model. It also helps to run post-transforms to get the final label mask.

Deepgrow 3D Training

This is an extension for train.py that redefines basic default arguments to run 3D training.

# Run to know all possible options
python ./train_3d.py -h

# Train a 3D Deepgrow model on single-gpu
python ./train_3d.py
    --input       deepgrow/3D/MSD_Task09_Spleen/dataset.json \
    --output      models/3D \
    --epochs      100

# Train a 3D Deepgrow model on multi-gpu (NVIDIA)
python -m torch.distributed.launch \
    --nproc_per_node=`nvidia-smi -L | wc -l` \
    --nnodes=1 \
    --node_rank=0 \
    --master_addr="localhost" \
    --master_port=1234 \
    -m train_3d \
    --multi_gpu true \
    --input       deepgrow/3D/MSD_Task09_Spleen/dataset.json \
    --output      models/3D \
    --epochs      100

# After training to export/save as torch script model
python ./train_3d.py
    --input       models/3D/model.pt \
    --output      models/3D/model.ts \
    --export      true

Deepgrow 3D Validation

This is an extension for validate.py that redefines basic default arguments to run 3D validation.

# Run to know all possible options
python ./validate_3d.py -h

# Evaluate a 3D Deepgrow model
python ./validate_3d.py
    --input       deepgrow/3D/MSD_Task09_Spleen/dataset.json \
    --output      eval/3D \
    --model_path  models/3D/model.pt

Deepgrow 3D Inference

This notebook helps to run any pre-transforms before running inference over a Deepgrow 3D model. It also helps to run post-transforms to get the final label mask.

Deepgrow Stats

It contains basic ignite handler to capture region/organ-wise statistics, save snapshots, outputs while running train/validation over a dataset that has multi-label mask. By-default the handler is added as part of training/validation steps.