One-class segmentation with 2D U-Net

In this tutorial we will learn the following features:

  • Training of a segmentation model (U-Net 2D) with a single label on multiple contrasts,

  • Testing of a trained model and computation of 3D evaluation metrics.

  • Visualization of the outputs of a trained model.

An interactive Colab version of this tutorial is directly accessible here: image_badge

Download dataset

We will use a publicly-available dataset consisting of MRI data of the spinal cord. This dataset is a subset of the spine-generic multi-center dataset and has been pre-processed to facilitate training/testing of a new model. Namely, for each subject, all six contrasts were co-registered together. Semi-manual cord segmentation for all modalities and manual cerebrospinal fluid labels for T2w modality were created. More details here.

In addition to the MRI data, this sample dataset also includes a trained model for spinal cord segmentation.

To download the dataset (~490MB), run the following commands in your terminal:

# Download data
ivadomed_download_data -d data_example_spinegeneric

Configuration file

In ivadomed, training is orchestrated by a configuration file. Examples of configuration files are available in the ivadomed/config/ folder and the documentation is available in Configuration File.

In this tutorial we will use the configuration file: ivadomed/config/config.json. First off, copy this configuration file in your local directory (to avoid modifying the source file):

cp <PATH_TO_IVADOMED>/ivadomed/config/config.json .

Then, open it with a text editor. Which you can view directly here: or you can see it in the collapsed JSON code block below.

Reveal the embedded `config.json`.
  1{
  2    "command": "train",
  3    "gpu_ids": [0],
  4    "path_output": "spineGeneric",
  5    "model_name": "my_model",
  6    "debugging": false,
  7    "object_detection_params": {
  8        "object_detection_path": null,
  9        "safety_factor": [1.0, 1.0, 1.0]
 10    },
 11    "loader_parameters": {
 12        "path_data": ["data_example_spinegeneric"],
 13        "subject_selection": {"n": [], "metadata": [], "value": []},
 14        "target_suffix": ["_seg-manual"],
 15        "extensions": [".nii.gz"],
 16        "roi_params": {
 17            "suffix": null,
 18            "slice_filter_roi": null
 19        },
 20        "contrast_params": {
 21            "training_validation": ["T1w", "T2w", "T2star"],
 22            "testing": ["T1w", "T2w", "T2star"],
 23            "balance": {}
 24        },
 25        "slice_filter_params": {
 26            "filter_empty_mask": false,
 27            "filter_empty_input": true
 28        },
 29        "slice_axis": "axial",
 30        "multichannel": false,
 31        "soft_gt": false
 32    },
 33    "split_dataset": {
 34        "fname_split": null,
 35        "random_seed": 6,
 36        "split_method" : "participant_id",
 37        "data_testing": {"data_type": null, "data_value":[]},
 38        "balance": null,
 39        "train_fraction": 0.6,
 40        "test_fraction": 0.2
 41    },
 42    "training_parameters": {
 43        "batch_size": 18,
 44        "loss": {
 45            "name": "DiceLoss"
 46        },
 47        "training_time": {
 48            "num_epochs": 100,
 49            "early_stopping_patience": 50,
 50            "early_stopping_epsilon": 0.001
 51        },
 52        "scheduler": {
 53            "initial_lr": 0.001,
 54            "lr_scheduler": {
 55                "name": "CosineAnnealingLR",
 56                "base_lr": 1e-5,
 57                "max_lr": 1e-2
 58            }
 59        },
 60        "balance_samples": {
 61            "applied": false,
 62            "type": "gt"
 63        },
 64        "mixup_alpha": null,
 65        "transfer_learning": {
 66            "retrain_model": null,
 67            "retrain_fraction": 1.0,
 68            "reset": true
 69        }
 70    },
 71    "default_model": {
 72        "name": "Unet",
 73        "dropout_rate": 0.3,
 74        "bn_momentum": 0.1,
 75        "final_activation": "sigmoid",
 76        "depth": 3
 77    },
 78    "FiLMedUnet": {
 79        "applied": false,
 80        "metadata": "contrasts",
 81        "film_layers": [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
 82    },
 83    "Modified3DUNet": {
 84        "applied": false,
 85        "length_3D": [128, 128, 16],
 86        "stride_3D": [128, 128, 16],
 87        "attention": false,
 88        "n_filters": 8
 89    },
 90    "uncertainty": {
 91        "epistemic": false,
 92        "aleatoric": false,
 93        "n_it": 0
 94    },
 95    "postprocessing": {
 96        "remove_noise": {"thr": -1},
 97        "keep_largest": {},
 98        "binarize_prediction": {"thr": 0.5},
 99        "uncertainty": {"thr": -1, "suffix": "_unc-vox.nii.gz"},
100        "fill_holes": {},
101        "remove_small": {"unit": "vox", "thr": 3}
102    },
103    "evaluation_parameters": {
104        "target_size": {"unit": "vox", "thr": [20, 100]},
105        "overlap": {"unit": "vox", "thr": 3}
106    },
107    "transformation": {
108        "Resample":
109        {
110            "hspace": 0.75,
111            "wspace": 0.75,
112            "dspace": 1
113        },
114        "CenterCrop": {
115            "size": [128, 128]},
116        "RandomAffine": {
117            "degrees": 5,
118            "scale": [0.1, 0.1],
119            "translate": [0.03, 0.03],
120            "applied_to": ["im", "gt"],
121            "dataset_type": ["training"]
122        },
123        "ElasticTransform": {
124            "alpha_range": [28.0, 30.0],
125            "sigma_range":  [3.5, 4.5],
126            "p": 0.1,
127            "applied_to": ["im", "gt"],
128            "dataset_type": ["training"]
129        },
130      "NormalizeInstance": {"applied_to": ["im"]}
131    }
132}

From this point onward, we will discuss some of the key parameters to perform a one-class 2D segmentation training. Most parameters are configurable only via modification of the configuration JSON file. For those that supports command line run time configuration, we included the respective command versions under the Command Line Interface tab

  • command: Action to perform. Here, we want to train a model:

    We can set the field within the newly copied config.json file as follow, at this line:

    "command": "train"
    
  • path_output: Folder name that will contain the output files (e.g., trained model, predictions, results).

    At this line in the config.json is where you can update the path_output.

    "path_output": "spineGeneric"
    
  • loader_parameters:path_data: Location of the dataset. As discussed in Data, the dataset should conform to the BIDS standard. Modify the path so it points to the location of the downloaded dataset.

    At this line in the config.json is where you can update the path_data within the loader_parameters sub-dictionary.

    "path_data": "data_example_spinegeneric"
    
  • loader_parameters:target_suffix: Suffix of the ground truth segmentation. The ground truth is located under the DATASET/derivatives/labels folder. In our case, the suffix is _seg-manual:

    At this line in the config.json is where you can update the target_suffix within the loader_parameters sub-dictionary.

    "target_suffix": ["_seg-manual"]
    
  • loader_parameters:contrast_params: Contrast(s) of interest

    At this line in the config.json is where you can update the contrast_params sub-dictionary within the loader_parameters sub-dictionary.

    "contrast_params": {
         "training_validation": ["T1w", "T2w", "T2star"],
         "testing": ["T1w", "T2w", "T2star"],
         "balance": {}
    }
    
  • loader_parameters:slice_axis: Orientation of the 2D slice to use with the model.

    At this line in the config.json is where you can update the slice_axis subkey within the loader_parameters sub-dictionary.

    "slice_axis": "axial"
    
  • loader_parameters:multichannel: Turn on/off multi-channel training. If true, each sample has several channels, where each channel is an image contrast. If false, only one image contrast is used per sample.

    At this line in the config.json is where you can update the multichannel subkey within the loader_parameters sub-dictionary.

    "multichannel": false
    

    Note

    The multichannel approach requires that for each subject, the image contrasts are co-registered. This implies that a ground truth segmentation is aligned with all contrasts, for a given subject. In this tutorial, only one channel will be used.

  • training_parameters:training_time:num_epochs: the maximum number of epochs that will be run during training. Each epoch is composed of a training part and an evaluation part. It should be a strictly positive integer.

    At this line in the config.json is where you can update the num_epochs subkey within the training_parameters:training_time sub-dictionary.

    "num_epochs": 100
    

Train model

Once the configuration file is ready, run the training:

ivadomed --train -c config.json --path-data path/to/bids/data --path-output path/to/output/directory
  • In the above command, we execute the --train command and manually specified --path-data and --path-output and overwrote/replace the specification in config.json

  • --train: We can pass other flags to execute different commands (training, testing, segmentation), see Usage.

  • --path-output: Folder name that will contain the output files (e.g., trained model, predictions, results).

  • --path-data: Location of the dataset. As discussed in Data, the dataset should conform to the BIDS standard. Modify the path so it points to the location of the downloaded dataset.

Note

If a compatible GPU is available, it will be used by default. Otherwise, training will use the CPU, which will take a prohibitively long computational time (several hours).

The main parameters of the training scheme and model will be displayed on the terminal, followed by the loss value on training and validation sets at every epoch. To know more about the meaning of each parameter, go to Configuration File. The value of the loss should decrease during the training.

Creating output path: spineGeneric
Cuda is not available.
Working on cpu.

Selected architecture: Unet, with the following parameters:
dropout_rate: 0.3
bn_momentum: 0.1
depth: 3
is_2d: True
final_activation: sigmoid
folder_name: my_model
in_channel: 1
out_channel: 1
Dataframe has been saved in spineGeneric\bids_dataframe.csv.
After splitting: train, validation and test fractions are respectively 0.6, 0.2 and 0.2 of participant_id.

Selected transformations for the ['training'] dataset:
Resample: {'hspace': 0.75, 'wspace': 0.75, 'dspace': 1}
CenterCrop: {'size': [128, 128]}
RandomAffine: {'degrees': 5, 'scale': [0.1, 0.1], 'translate': [0.03, 0.03], 'applied_to': ['im', 'gt']}
ElasticTransform: {'alpha_range': [28.0, 30.0], 'sigma_range': [3.5, 4.5], 'p': 0.1, 'applied_to': ['im', 'gt']}
NumpyToTensor: {}
NormalizeInstance: {'applied_to': ['im']}

Selected transformations for the ['validation'] dataset:
Resample: {'hspace': 0.75, 'wspace': 0.75, 'dspace': 1}
CenterCrop: {'size': [128, 128]}
NumpyToTensor: {}
NormalizeInstance: {'applied_to': ['im']}
Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 383.65it/s]
Loaded 92 axial slices for the validation set.
Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17/17 [00:00<00:00, 282.10it/s]
Loaded 276 axial slices for the training set.
Creating model directory: spineGeneric\my_model

Initialising model's weights from scratch.

Scheduler parameters: {'name': 'CosineAnnealingLR', 'base_lr': 1e-05, 'max_lr': 0.01}

Selected Loss: DiceLoss
with the parameters: []
Epoch 1 training loss: -0.0336.
Epoch 1 validation loss: -0.0382.

After 100 epochs (see num_epochs in the configuration file), the Dice score on the validation set should be ~90%.

Evaluate model

To test the trained model on the testing sub-dataset and compute evaluation metrics, run:

ivadomed --test -c config.json --path-data path/to/bids/data --path-output path/to/output/directory

The model’s parameters will be displayed in the terminal, followed by a preview of the results for each image. The resulting segmentation is saved for each image in the <PATH_TO_OUT_DIR>/pred_masks while a csv file, saved in <PATH_TO_OUT_DIR>/results_eval/evaluation_3Dmetrics.csv, contains all the evaluation metrics. For more details on the evaluation metrics, see ivadomed.metrics.

Output path already exists: spineGeneric
Cuda is not available.
Working on cpu.

Selected architecture: Unet, with the following parameters:
dropout_rate: 0.3
bn_momentum: 0.1
depth: 3
is_2d: True
final_activation: sigmoid
folder_name: my_model
in_channel: 1
out_channel: 1
Dataframe has been saved in spineGeneric\bids_dataframe.csv.
After splitting: train, validation and test fractions are respectively 0.6, 0.2 and 0.2 of participant_id.

Selected transformations for the ['testing'] dataset:
Resample: {'hspace': 0.75, 'wspace': 0.75, 'dspace': 1}
CenterCrop: {'size': [128, 128]}
NumpyToTensor: {}
NormalizeInstance: {'applied_to': ['im']}
Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 373.59it/s]
Loaded 94 axial slices for the testing set.

Loading model: spineGeneric\best_model.pt
Inference - Iteration 0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:29<00:00,  4.86s/it]
{'dice_score': 0.9334570551249012, 'multi_class_dice_score': 0.9334570551249012, 'precision_score': 0.925126264682505, 'recall_score': 0.9428409070673442, 'specificity_score': 0.9999025807354961, 'intersection_over_union': 0.8756498644456311, 'accu
racy_score': 0.9998261755671077, 'hausdorff_score': 0.05965616760384793}

Run Evaluation on spineGeneric\pred_masks

Evaluation: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:05<00:00,  1.04it/s]
                  avd_class0  dice_class0  lfdr_101-INFvox_class0  lfdr_class0  ltpr_101-INFvox_class0  ltpr_class0  mse_class0  ...  n_pred_class0  precision_class0  recall_class0  rvd_class0  specificity_class0  vol_gt_class0  vol_pred_class0
image_id                                                                                                                            ...
sub-mpicbs06_T1w       0.086296     0.940116                     0.0          0.0                     1.0          1.0    0.002292  ...            1.0          0.902774       0.980680   -0.086296            0.999879    4852.499537      5271.249497
sub-mpicbs06_T2star    0.038346     0.909164                     0.0          0.0                     1.0          1.0    0.003195  ...            1.0          0.892377       0.926595   -0.038346            0.999871    4563.749565      4738.749548
sub-mpicbs06_T2w       0.032715     0.947155                     0.0          0.0                     1.0          1.0    0.001971  ...            1.0          0.932153       0.962648   -0.032715            0.999920    4852.499537      5011.249522
sub-unf01_T1w          0.020288     0.954007                     0.0          0.0                     1.0          1.0    0.002164  ...            1.0          0.944522       0.963684   -0.020288            0.999917    6161.249412      6286.249400
sub-unf01_T2star       0.001517     0.935124                     0.0          0.0                     1.0          1.0    0.002831  ...            1.0          0.934416       0.935834   -0.001517            0.999904    5766.249450      5774.999449

[5 rows x 16 columns]

The test image segmentations are stored in <PATH_TO_OUT_DIR>/pred_masks/ and have the same name as the input image with the suffix _pred. To visualize the segmentation of a given subject, you can use any Nifti image viewer. For FSLeyes users, this command will open the input image with the overlaid prediction (segmentation) for one of the test subject:

fsleyes <PATH_TO_BIDS_DATA>/sub-mpicbs06/anat/sub-mpicbs06_T2w.nii.gz <PATH_TO_OUT_DIR>/pred_masks/sub-mpicbs06_T2w_pred.nii.gz -cm red

After the training for 100 epochs, the segmentations should be similar to the one presented in the following image. The output and ground truth segmentations of the spinal cord are presented in red (subject sub-mpicbs06 with contrast T2w):

https://raw.githubusercontent.com/ivadomed/doc-figures/main/tutorials/one_class_segmentation_2d_unet/sc_prediction.png