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New: Please check out SFDA, the repository of our Source-Free Domain Adaptation method.

Constrained Domain Adaptation

We introduce Constrained Domain Adaptation for Image Segmentation.

Mathilde Bateson, Hoel Kervadec, Jose Dolz, Hervé Lombaert, Ismail Ben Ayed @ETS Montréal

Please cite our paper if you find it useful for your research.

@ARTICLE{BatesonCDA,
  author={Bateson, M. and Dolz, J. and Kervadec, H. and Lombaert, H. and Ayed, I. Ben},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Constrained Domain Adaptation for Image Segmentation}, 
  year={2021},
  volume={40},
  number={7},
  pages={1875-1887}}

Example Results

Requirements

Non-exhaustive list:

  • python3.6+
  • Pytorch 1.0
  • nibabel
  • Scipy
  • NumPy
  • Matplotlib
  • Scikit-image
  • zsh

Data scheme

datasets

For instance

data
    mr/
	    train/
         IMG/
             slice10_0.nii
             ...
         GT/
             slice10_0.nii
             ...
		...
	    val/
         IMG/
             slice100_0.nii
             ...
         GT/
             slice100_0.nii
             ...
         ...
    ct/
	    train/
        IMG/
            ctslice1_0.nii
            ...
        GT/
            ctslice1_0.nii
            ...
        ...
	    val/
         IMG/
             ctslice11_0.nii
             ...
         GT/
             ctslice11_0.nii
             ...
		...

The network takes png or nii files as an input. The gt folder contains gray-scale images of the ground-truth, where the gray-scale level is the number of the class (0,1,...K).

Class-ratio (sizes) prior

The class-ratio prior is estimated for each slice in the target domain training and validation sets. It estimated once, before the start of the adaptation phase, and saved in a csv file. In our implementation, is it estimated through an auxiliary network, but is can as easily be estimated from anatomical knowledge. We provide these simple estimations in the sizes folder.

Scheme

sizes/
    whs.csv
    ivd.csv

The size csv file should be organized as follows:

val_ids dumbpredwtags
ctslice00_0.nii [Estimated_Size_class0, Estimated_Size_class1, ..., Estimated_Size_classk]

Sample from sizes/whs.csv :

val_ids val_gt_size dumbpredwtags
ctslice00_0.nii [147398.0, 827.0] [140225, 6905]
ctslice00_1.nii [147080.0, 1145.0] [140225, 6905]
ctslice00_14.nii [148225.0, 0.0] [148225, 0]

NB 1 : there should be no overlap between names of the slices in the training and validation sets (Case00_0.nii,...).

NB 2: in our implementation, the csv file contains the sizes priors in pixels, and the Quadratic Loss divides the size in pixels by (w*h) the height and weight of the slice, to obtain the class-ratio prior.

NB 3: Estimated_Size_class0 + Estimated_Size_class1 + ... + Estimated_Size_classk = w*h

NB 4: the true val_gt_size is unknown, so it is not directly used in our proposed CDA. However, in our framework an image-level annotation is available for the target training dataset: the "Tag" of each class k, indicating the presence or absence of class k in the slice. Therefore, Estimated_Size_classk=0 if val_gt_size_k = 0 and Estimated_Size_classk>0 if val_gt_size_k > 0

NB 5: To have an idea of the capacity of the SFDA model in the ideal case where the ground truth class-ratio prior is known, it is useful to run the upper bound model CDA_TrueSize choosing the column "val_gt_size" instead of "dumbpredwtags". This can be changed in the makefile :

results/whs/CDA_TrueSize: OPT = --target_losses="[('EntKLProp', {'inv_consloss':True,'lamb_se':1,'lamb_consprior':1,'ivd':True,'weights_se':[0.1,0.9],'idc_c': [1],'curi':True,'power': 1},'PredictionBounds', \
      {'margin':0,'dir':'high','idc':[0,1],'predcol':'val_gt_size','power': 1, 'mode':'percentage','sizefile':'sizes/whs.csv'},'norm_soft_size',1)]" \
           --val_target_folders="$(TT_DATA)"  --l_rate 0.000001 --n_epoch 100 --lr_decay 0.9 --batch_size 10 --target_folders="$(TT_DATA)" --model_weights="$(M_WEIGHTS_ul)" \

NB 6 : If you change the name of the columns (val_ids, dumbpredwtags) in the size file, you should change them in the bounds.py file as well as in the ivd.make makefile.

results

results/
    whs/
        fs/
            best_epoch_3d/
                val/
                    ctslice11_0.png
                    ...
            iter000/
                val/
            ...
        cda/
            ...
        params.txt # saves all the argparse parameters of the model 
	best_3d.pkl # best model saved
	last.pkl # last epoch
        IMG_target_metrics.csv # metrics over time, csv
        3dbestepoch.txt # number and 3D Dice of the best epoch 
        ...
    whs/
        ...
archives/
    $(REPO)-$(DATE)-$(HASH)-$(HOSTNAME)-cda.tar.gz
    $(REPO)-$(DATE)-$(HASH)-$(HOSTNAME)-fs.tar.gz

Interesting bits

The losses are defined in the losses.py file.

Running our main experiment

Once you have downladed the data and organized it such as in the scheme above, run the main experiment as follows:

make -f whs.make 

This will first run the source training model, which will be saves in results/cesource, and then the CDA model, which will be saved in results/cda.

Cool tricks

Remove all assertions from the code to speed up. Usually done after making sure it does not crash for one complete epoch:

make -f whs.make <anything really> CFLAGS=-O

Use a specific python executable:

make -f whs.make <super target> CC=/path/to/the/executable

Train for only 5 epochs, with a dummy network, and only 10 images per data loader. Useful for debugging:

make -f whs.make <anything really> NET=Dimwit EPC=5 DEBUG=--debug

Rebuild everything even if already exist:

make -f whs.make <a> -B

Only print the commands that will be run (useful to check recipes are properly defined):

make -f whs.make <a> -n

Related Implementation and Dataset

  • Mathilde Bateson, Hoel Kervadec, Jose Dolz, Hervé Lombaert, Ismail Ben Ayed. Source-Relaxed Domain Adaptation for Image Segmentation. In MICCAI 2020. [paper] [implementation]
  • Hoel Kervadec, Jose Dolz, Meng Tang, Eric Granger, Yuri Boykov, Ismail Ben Ayed. Constrained-CNN losses for weakly supervised segmentation. In Medical Image Analysis, 2019. [paper] [code]
  • Heart Dataset and details: We used the preprocessed dataset from Dou et al. : https://github.com/carrenD/Medical-Cross-Modality-Domain-Adaptation. The data is in tfs records, it should be transformed to nii or png before running the makefile. We used a randomized sequence of augmentation steps (contrast shifts, flips) as a data augmentation strategy in the source domain. We did not use any augmentation for the target domain.
  • Spine Dataset and details: https://ivdm3seg.weebly.com/ . From the original coronal view, we transposed the slices to transverse view in our experiments. We set the water modality (Wat) as the source and the in-phase (IP) modality as the target domain. From this dataset, 13 scans are used for training, and the remaining 3 scans for validation.

Download the data and put in the data/sagittal folder, then rotate and save into the data/ivd_transverse folder, both for the Wat and the IP modality:

python rotate.py  --base_folder='./data/sagittal/IP/' --folders=['train','val'] --save_folder='./data/ivd_transverse/IP/' --rot=’rot’  --grp_regex="Subj_\\d+_"
  • New: Prostate Dataset and details: https://raw.githubusercontent.com/liuquande/SAML/. The SA site dataset was used a target domain, the SB site was used as source domain. For both datasets, we use 20 scans for training, and the remaining 10 scans for validation.

Note

The model and code are available for non-commercial research purposes only.

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