Users can explore the use of various UNet inspired architectures integrated into the nnUNet framework, and compare performance of architectural variations in a consistent and transperent fashion, with network architecture being the only independent variable between comparisons with respect to a single dataset. For more details refer to the original article:
Paper coming soon ...
nnUNet was developed by Isensee et al. and further information on the original framework may be found by reading the following original paper:
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2020). nnU-Net: a self-configuring method
for deep learning-based biomedical image segmentation. Nature Methods, 1-9.
The nnUNet is a fully automated and generalisable framework which automatically configures the full training pipeline for any 3D medical imaging segmentation task it is applied on, taking into account dataset properties and hardware constraints.
The nnUNet utilises a standard UNet type architecture which is self-configuring in terms of both depth and hyperparameters. We extend the original nnUNet code by integrating features found in more advanced UNet variations, namely residual blocks, dense blocks, and inception blocks.
Note: If making use of this source code please reference both the above papers.
Below a brief guide to using the modified nnUNet framework is presented which is based on the original nnUNet guide by Isensee et al. For a more detailed/insightful explanation please refer to the original nnUNet repository.
Note: The code in this repository is derived from the original nnUNet repository and is identical except for the modification/addition of the following files: experiment_planner_dense3DUNet_v21.py, experiment_planner_inception3DUNet_v21.py, experiment_planner_residual3DUNet_v21.py, experiment_planner_SpatialMultiAttention_3DUNet_v21.py, experiment_planner_SpatialSingleAttention_3DUNet_v21.py, experiment_planner_ChannelSpatialAttention_3DUNet_v21.py, conv_blocks.py, generic_modular_custom_UNet.py, generic_modular_UNet.py, nnUNetTrainerV2_DenseUNet.py, nnUNetTrainerV2_InceptionUNet.py, nnUNetTrainerV2_ResidualUNet.py, nnUNetTrainerV2_GenericSpatialMultiAttentionUNet.py, nnUNetTrainerV2_GenericSpatialSingleAttentionUNet.py, nnUNetTrainerV2_GenericChannelSpatialAttentionUNet.py.
The following instructions are specific to the running of the nnUNet integrated Residual, Inception, and Dense variations of the UNet.
To install, clone the git page and use pip install. Make sure latest version of PyTorch is installed.
git clone https://github.com/niccolo246/Advanced_nnUNet
cd Advanced_nnUNet
pip install -e .
Ensure data is in correct format compatible with nnUNet - refer to original nnUNet page for details. Furthermore paths and relevant folders must be correctly set up as shown here.
To commence experiment planning perform following steps:
nnUNet_plan_and_preprocess -t TASK_ID nnUNet_plan_and_preprocess -t TASK_ID -pl3d ExperimentPlanner3DResidualUNet_v21nnUNet_plan_and_preprocess -t TASK_ID -pl3d ExperimentPlanner3DInceptionUNet_v21nnUNet_plan_and_preprocess -t TASK_ID -pl3d ExperimentPlanner3DDenseUNet_v21nnUNet_plan_and_preprocess -t TASK_ID -pl3d ExperimentPlanner3DSpatialSingleAttentionUNet_v21nnUNet_plan_and_preprocess -t TASK_ID -pl3d ExperimentPlanner3DSpatialMultiAttentionUNet_v21nnUNet_plan_and_preprocess -t TASK_ID -pl3d ExperimentPlanner3DChannelSpatialAttentionUNet_v21We here concentrate on training demonstrations using the 3D full-resolution configuration for the UNet architecture variant.
Run the following depending on which architecture one wishes to experiment training with:
For FOLD in [0, 1, 2, 3, 4], run:
nnUNet_train 3d_fullres nnUNetTrainerV2_ResidualUNet TASK_NAME_OR_ID FOLD -p nnUNetPlans_ResidualUNet_v2.1For FOLD in [0, 1, 2, 3, 4], run:
nnUNet_train 3d_fullres nnUNetTrainerV2_InceptionUNet TASK_NAME_OR_ID FOLD -p nnUNetPlans_InceptionUNet_v2.1For FOLD in [0, 1, 2, 3, 4], run:
nnUNet_train 3d_fullres nnUNetTrainerV2_DenseUNet TASK_NAME_OR_ID FOLD -p nnUNetPlans_DenseUNet_v2.1For FOLD in [0, 1, 2, 3, 4], run:
nnUNet_train 3d_fullres nnUNetTrainerV2_SpatialSingleAttentionUNet TASK_NAME_OR_ID FOLD -p nnUNetPlans_SpatialSingleAttentionUNet_v2.1For FOLD in [0, 1, 2, 3, 4], run:
nnUNet_train 3d_fullres nnUNetTrainerV2_SpatialMultiAttentionUNet TASK_NAME_OR_ID FOLD -p nnUNetPlans_SpatialMultiAttentionUNet_v2.1For FOLD in [0, 1, 2, 3, 4], run:
nnUNet_train 3d_fullres nnUNetTrainerV2_ChannelSpatialAttentionUNet TASK_NAME_OR_ID FOLD -p nnUNetPlans_ChannelSpatialAttentionUNet_v2.1Note: as discussed in the original nnUNet repository, one does not have to run training on all folds for inference to run (running full training on one fold only is sufficient).
We here concentrate on inference demonstrations using the 3D full-resolution configuration for the UNet architecture variant.
Run the following depending on which architecture one wishes to experiment inference with:
nnUNet_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -t TASK_NAME_OR_ID -m 3d_fullres -p nnUNetPlans_ResidualUNet_v2.1 -tr nnUNetTrainerV2_ResidualUNetnnUNet_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -t TASK_NAME_OR_ID -m 3d_fullres -p nnUNetPlans_InceptionUNet_v2.1 -tr nnUNetTrainerV2_InceptionUNetnnUNet_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -t TASK_NAME_OR_ID -m 3d_fullres -p nnUNetPlans_DenseUNet_v2.1 -tr nnUNetTrainerV2_DenseUNetnnUNet_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -t TASK_NAME_OR_ID -m 3d_fullres -p nnUNetPlans_SpatialSingleAttentionUNet_v2.1 -tr nnUNetTrainerV2_SpatialSingleAttentionUNetnnUNet_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -t TASK_NAME_OR_ID -m 3d_fullres -p nnUNetPlans_SpatialMultiAttentionUNet_v2.1 -tr nnUNetTrainerV2_SpatialMultiAttentionUNetnnUNet_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -t TASK_NAME_OR_ID -m 3d_fullres -p nnUNetPlans_ChannelSpatialAttentionUNet_v2.1 -tr nnUNetTrainerV2_ChannelSpatialAttentionUNetNote: For information on network ensembling refer to original nnUNet repository.