pytorch-3dunet

PyTorch implementation of 3D U-Net and its variants:

• UNet3D Standard 3D U-Net based on 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

• ResidualUNet3D Residual 3D U-Net based on Superhuman Accuracy on the SNEMI3D Connectomics Challenge

• ResidualUNetSE3D Similar to ResidualUNet3D with the addition of Squeeze and Excitation blocks based on Deep Learning Semantic Segmentation for High-Resolution Medical Volumes. Original squeeze and excite paper: Squeeze-and-Excitation Networks

The code allows for training the U-Net for both: semantic segmentation (binary and multi-class) and regression problems (e.g. de-noising, learning deconvolutions).

2D U-Net

2D U-Net is also supported, see 2DUnet_confocal or 2DUnet_dsb2018 for example configuration. Just make sure to keep the singleton z-dimension in your H5 dataset (i.e. (1, Y, X) instead of (Y, X)) , because data loading / data augmentation requires tensors of rank 3. The 2D U-Net itself uses the standard 2D convolutional layers instead of 3D convolutional with kernel size (1, 3, 3) for performance reasons.

Input Data Format

The input data should be stored in HDF5 files. The HDF5 files for training should contain two datasets: raw and label (and optionally weights dataset). The raw dataset should contain the input data, while the label dataset should contain the ground truth labels (optional weights dataset should contain the values for weighting the loss function in different regions of the input). The format of the raw and label datasets depends on whether the problem is 2D or 3D and whether the data is single-channel or multi-channel, see the table below:

Prerequisites

• Linux
• NVIDIA GPU
• CUDA CuDNN

Running on Windows

The package has not been tested on Windows, however some users reported using it successfully on Windows.

Installation

• The easiest way to install pytorch-3dunet package is via conda/mamba:

conda install -c conda-forge mamba
mamba create -n pytorch3dunet -c pytorch -c nvidia -c conda-forge -c awolny pytorch-3dunet
conda activate pytorch3dunet

After installation the following commands are accessible within the conda environment: train3dunet for training the network and predict3dunet for prediction (see below).

• One can also install directly from source:

python setup.py install

Installation tips

Make sure that the installed pytorch is compatible with your CUDA version, otherwise the training/prediction will fail to run on GPU.

Train

Given that pytorch-3dunet package was installed via conda as described above, one can train the network by simply invoking:

train3dunet --config <CONFIG>

where CONFIG is the path to a YAML configuration file, which specifies all aspects of the training procedure.

In order to train on your own data just provide the paths to your HDF5 training and validation datasets in the config.

• sample config for 3D semantic segmentation (cell boundary segmentation): train_config_segmentation.yaml
• sample config for 3D regression task (denoising): train_config_regression.yaml

The HDF5 files should contain the raw/label data sets in the following axis order: DHW (in case of 3D) CDHW (in case of 4D).

One can monitor the training progress with Tensorboard tensorboard --logdir <checkpoint_dir>/logs/ (you need tensorflow installed in your conda env), where checkpoint_dir is the path to the checkpoint directory specified in the config.

Training tips

1. When training with binary-based losses, i.e.: BCEWithLogitsLoss, DiceLoss, BCEDiceLoss, GeneralizedDiceLoss: The target data has to be 4D (one target binary mask per channel). When training with WeightedCrossEntropyLoss, CrossEntropyLoss, PixelWiseCrossEntropyLoss the target dataset has to be 3D, see also pytorch documentation for CE loss: https://pytorch.org/docs/master/generated/torch.nn.CrossEntropyLoss.html
2. final_sigmoid in the model config section applies only to the inference time (validation, test):
   • When training with BCEWithLogitsLoss, DiceLoss, BCEDiceLoss, GeneralizedDiceLoss set final_sigmoid=True
   • When training with cross entropy based losses (WeightedCrossEntropyLoss, CrossEntropyLoss, PixelWiseCrossEntropyLoss) set final_sigmoid=False so that Softmax normalization is applied to the output.

Prediction

Given that pytorch-3dunet package was installed via conda as described above, one can run the prediction via:

predict3dunet --config <CONFIG>

In order to predict on your own data, just provide the path to your model as well as paths to HDF5 test files (see example test_config_segmentation.yaml).

Prediction tips

In order to avoid patch boundary artifacts in the output prediction masks the patch predictions are averaged, so make sure that patch/stride params lead to overlapping blocks, e.g. patch: [64, 128, 128] stride: [32, 96, 96] will give you a 'halo' of 32 voxels in each direction.

Data Parallelism

By default, if multiple GPUs are available training/prediction will be run on all the GPUs using DataParallel. If training/prediction on all available GPUs is not desirable, restrict the number of GPUs using CUDA_VISIBLE_DEVICES, e.g.

CUDA_VISIBLE_DEVICES=0,1 train3dunet --config <CONFIG>

or

CUDA_VISIBLE_DEVICES=0,1 predict3dunet --config <CONFIG>

Supported Loss Functions

Semantic Segmentation

• BCEWithLogitsLoss (binary cross-entropy)
• DiceLoss (standard DiceLoss defined as 1 - DiceCoefficient used for binary semantic segmentation; when more than 2 classes are present in the ground truth, it computes the DiceLoss per channel and averages the values)
• BCEDiceLoss (Linear combination of BCE and Dice losses, i.e. alpha * BCE + beta * Dice, alpha, beta can be specified in the loss section of the config)
• CrossEntropyLoss (one can specify class weights via the weight: [w_1, ..., w_k] in the loss section of the config)
• PixelWiseCrossEntropyLoss (one can specify per pixel weights in order to give more gradient to the important/under-represented regions in the ground truth)
• WeightedCrossEntropyLoss (see 'Weighted cross-entropy (WCE)' in the below paper for a detailed explanation)
• GeneralizedDiceLoss (see 'Generalized Dice Loss (GDL)' in the below paper for a detailed explanation) Note: use this loss function only if the labels in the training dataset are very imbalanced e.g. one class having at least 3 orders of magnitude more voxels than the others. Otherwise use standard DiceLoss.

For a detailed explanation of some of the supported loss functions see: Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations.

Regression

• MSELoss (mean squared error loss)
• L1Loss (mean absolute errro loss)
• SmoothL1Loss (less sensitive to outliers than MSELoss)
• WeightedSmoothL1Loss (extension of the SmoothL1Loss which allows to weight the voxel values above/below a given threshold differently)

Supported Evaluation Metrics

Semantic Segmentation

• MeanIoU (mean intersection over union)
• DiceCoefficient (computes per channel Dice Coefficient and returns the average) If a 3D U-Net was trained to predict cell boundaries, one can use the following semantic instance segmentation metrics (the metrics below are computed by running connected components on thresholded boundary map and comparing the resulted instances to the ground truth instance segmentation):
• BoundaryAveragePrecision (Average Precision applied to the boundary probability maps: thresholds the output from the network, runs connected components to get the segmentation and computes AP between the resulting segmentation and the ground truth)
• AdaptedRandError (see http://brainiac2.mit.edu/SNEMI3D/evaluation for a detailed explanation)
• AveragePrecision (see https://www.kaggle.com/stkbailey/step-by-step-explanation-of-scoring-metric)

If not specified MeanIoU will be used by default.

Regression

• PSNR (peak signal to noise ratio)
• MSE (mean squared error)

Examples

Cell boundary predictions for lightsheet images of Arabidopsis thaliana lateral root

Training/predictions configs can be found in 3DUnet_lightsheet_boundary. Pre-trained model weights available here. In order to use the pre-trained model on your own data:

• download the best_checkpoint.pytorch from the above link
• add the path to the downloaded model and the path to your data in test_config.yml
• run predict3dunet --config test_config.yml
• optionally fine-tune the pre-trained model with your own data, by setting the pre_trained attribute in the YAML config to point to the best_checkpoint.pytorch path

The data used for training can be downloaded from the following OSF project:

• training set: https://osf.io/9x3g2/
• validation set: https://osf.io/vs6gb/
• test set: https://osf.io/tn4xj/

Sample z-slice predictions on the test set (top: raw input , bottom: boundary predictions):

Cell boundary predictions for confocal images of Arabidopsis thaliana ovules

Training/predictions configs can be found in 3DUnet_confocal_boundary. Pre-trained model weights available here. In order to use the pre-trained model on your own data:

• download the best_checkpoint.pytorch from the above link
• add the path to the downloaded model and the path to your data in test_config.yml
• run predict3dunet --config test_config.yml
• optionally fine-tune the pre-trained model with your own data, by setting the pre_trained attribute in the YAML config to point to the best_checkpoint.pytorch path

The data used for training can be downloaded from the following OSF project:

• training set: https://osf.io/x9yns/
• validation set: https://osf.io/xp5uf/
• test set: https://osf.io/8jz7e/

Sample z-slice predictions on the test set (top: raw input , bottom: boundary predictions):

Nuclei predictions for lightsheet images of Arabidopsis thaliana lateral root

Training/predictions configs can be found in 3DUnet_lightsheet_nuclei. Pre-trained model weights available here. In order to use the pre-trained model on your own data:

• download the best_checkpoint.pytorch from the above link
• add the path to the downloaded model and the path to your data in test_config.yml
• run predict3dunet --config test_config.yml
• optionally fine-tune the pre-trained model with your own data, by setting the pre_trained attribute in the YAML config to point to the best_checkpoint.pytorch path

The training and validation sets can be downloaded from the following OSF project: https://osf.io/thxzn/

Sample z-slice predictions on the test set (top: raw input, bottom: nuclei predictions):

2D nuclei predictions for Kaggle DSB2018

The data can be downloaded from: https://www.kaggle.com/c/data-science-bowl-2018/data

Training/predictions configs can be found in 2DUnet_dsb2018.

Sample predictions on the test image (top: raw input, bottom: nuclei predictions):

Contribute

If you want to contribute back, please make a pull request.

Cite

If you use this code for your research, please cite as:

@article {10.7554/eLife.57613,
article_type = {journal},
title = {Accurate and versatile 3D segmentation of plant tissues at cellular resolution},
author = {Wolny, Adrian and Cerrone, Lorenzo and Vijayan, Athul and Tofanelli, Rachele and Barro, Amaya Vilches and Louveaux, Marion and Wenzl, Christian and Strauss, Sören and Wilson-Sánchez, David and Lymbouridou, Rena and Steigleder, Susanne S and Pape, Constantin and Bailoni, Alberto and Duran-Nebreda, Salva and Bassel, George W and Lohmann, Jan U and Tsiantis, Miltos and Hamprecht, Fred A and Schneitz, Kay and Maizel, Alexis and Kreshuk, Anna},
editor = {Hardtke, Christian S and Bergmann, Dominique C and Bergmann, Dominique C and Graeff, Moritz},
volume = 9,
year = 2020,
month = {jul},
pub_date = {2020-07-29},
pages = {e57613},
citation = {eLife 2020;9:e57613},
doi = {10.7554/eLife.57613},
url = {https://doi.org/10.7554/eLife.57613},
keywords = {instance segmentation, cell segmentation, deep learning, image analysis},
journal = {eLife},
issn = {2050-084X},
publisher = {eLife Sciences Publications, Ltd},
}