Moeinh77 / pytorch-3dunet

3D U-Net model for volumetric semantic segmentation written in pytorch

pytorch-3dunet

PyTorch implementation 3D U-Net and its variants:

• Standard 3D U-Net based on 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation Özgün Çiçek et al.

• Residual 3D U-Net based on Superhuman Accuracy on the SNEMI3D Connectomics Challenge Kisuk Lee et al.

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

Training the standard 2D U-Net is also possible, see 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 always.

Prerequisites

• Linux
• NVIDIA GPU
• CUDA CuDNN

Running on Windows

The package has not been tested on Windows, however some reported using it on Windows. One thing to keep in mind: when training with CrossEntropyLoss: the label type in the config file should be change from long to int64, otherwise there will be an error: RuntimeError: Expected object of scalar type Long but got scalar type Int for argument #2 'target'.

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 weight: [w_1, ..., w_k] in the loss section of the config)
• PixelWiseCrossEntropyLoss (one can specify not only class weights but also per pixel weights in order to give more gradient to important (or under-represented) regions in the ground truth)
• WeightedCrossEntropyLoss (see 'Weighted cross-entropy (WCE)' in the below paper for a detailed explanation; one can specify class weights via weight: [w_1, ..., w_k] in the loss section of the config)
• GeneralizedDiceLoss (see 'Generalized Dice Loss (GDL)' in the below paper for a detailed explanation; one can specify class weights via weight: [w_1, ..., w_k] in the loss section of the config). 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 Carole H. Sudre, Wenqi Li, Tom Vercauteren, Sebastien Ourselin, M. Jorge Cardoso

IMPORTANT: if one wants to use their own loss function, bear in mind that the current model implementation always output logits and it's up to the implementation of the loss to normalize it correctly, e.g. by applying Sigmoid or Softmax.

Regression

• MSELoss
• L1Loss
• SmoothL1Loss
• 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 - Dice Coefficient (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 boundary maps given by the network, runs connected components to get the segmentation and computes AP between the resulting segmentation and the ground truth
• AdaptedRandError - Adapted Rand Error (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

Installation

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

conda create -n 3dunet -c conda-forge -c awolny pytorch-3dunet
conda activate 3dunet

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. You can re-install pytorch compatible with your CUDA in the 3dunet env by:

conda install -c pytorch torchvision cudatoolkit=<YOU_CUDA_VERSION> pytorch

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: train_config_dice.yaml)
• sample config for 3D regression task: 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). If you have a 3D binary data (foreground/background), you can just change ToTensor transform for the label to contain expand_dims: true, see e.g. train_config_dice.yaml. 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: When training with cross entropy based losses (WeightedCrossEntropyLoss, CrossEntropyLoss, PixelWiseCrossEntropyLoss) set final_sigmoid=False so that Softmax normalization is applied to the output. When training with BCEWithLogitsLoss, DiceLoss, BCEDiceLoss, GeneralizedDiceLoss set final_sigmoid=True

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 test_config_dice.yaml).

Prediction tips

In order to avoid checkerboard 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>

Examples

Cell boundary predictions for lightsheet images of Arabidopsis thaliana lateral root

The data 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/

Training and inference configs can be found in 3DUnet_lightsheet_boundary.

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

Cell boundary predictions for confocal images of Arabidopsis thaliana ovules

The data 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/

Training and inference configs can be found in 3DUnet_confocal_boundary.

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

Nuclei predictions for lightsheet images of Arabidopsis thaliana lateral root

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

Training and inference configs can be found in 3DUnet_lightsheet_nuclei.

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 and inference 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},
abstract = {Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.},
keywords = {instance segmentation, cell segmentation, deep learning, image analysis},
journal = {eLife},
issn = {2050-084X},
publisher = {eLife Sciences Publications, Ltd},
}