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https://github.com/calico/ukbb-mri-sseg

UKBB MRI semantic segmentation for Abdominal Dixon and other modalities
https://github.com/calico/ukbb-mri-sseg

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UKBB MRI semantic segmentation for Abdominal Dixon and other modalities

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# ukbb-mri-sseg

UKBB MRI semantic segmentation for Abdominal Dixon and other modalities



This repository uses [git lfs](https://git-lfs.github.com/) for trained model files (inside `trained_models`).

Please use `git lfs pull` or `git lfs clone`.



## Worked Example with processed UKBB Data

We use an external published repository to process the raw UKBB data:

https://github.com/recoh/pipeline/



Please follow the installation instructions there to download and install.

After installing, activate the command line environment, and download process these datasets.

The following files will be created. `ESS3TE4SJ3DEP5C5` is a subject id. Please place the subject ID folder inside a `processed_nifti` folder for the purpose of this example. 

```

ESS3TE4SJ3DEP5C5/

├── nifti

│   ├── fat.nii.gz

│   ├── ideal_liver_magnitude.nii.gz

│   ├── ideal_liver_phase.nii.gz

│   ├── ip.nii.gz

│   ├── mask.nii.gz

│   ├── multiecho_pancreas_magnitude.nii.gz

│   ├── multiecho_pancreas_phase.nii.gz

│   ├── op.nii.gz

│   ├── t1_vibe_pancreas.nii.gz

│   ├── t1_vibe_pancreas_norm.nii.gz

│   └── water.nii.gz

```



### Install and create virtual environment

Please exit the CLI interface from `pipeline_private` repository, and activate the python virtual environment associated with this repository 

```

python3 -m venv env

source env/bin/activate

pip install -U pip

pip install -r requirements.txt

```



We set up environment variables to be used in this worked example:

```

source env/bin/activate

SUBJECT_ID=ESS3TE4SJ3DEP5C5

echo $SUBJECT_ID > example_ids_list.txt

PROCESSED_NIFTI_DIR=processed_nifti

OUTPUT_FOLDER=/tmp/ukbb_mri_sseg_output

```



We download segmentation model weights. In the case of DIXON weights a further extraction step is needed.

```

git lfs pull

cat trained_models/knee_to_neck_dixon_split.tar.* | tar -xvf - -C trained_models/

```



The `tar` extraction command above might not work all systems. Alternatively,

```

cd trained_models

cat knee_to_neck_dixon_split.tar.* | tar -xvf -

cd ..

```



### Neck-to-knee DIXON Segmentation



This command performs inference on dixon data. See `/tmp/ukbb_mri_sseg_output` for output

```



DIXON_NIFTI_DIR=$PROCESSED_NIFTI_DIR/$SUBJECT_ID/nifti

RESTORE_STRING=trained_models/knee_to_neck_dixon/20200401-mpgp118-best_xe/model.ckpt-20000

python3 public_sseg/infer_knee_to_neck_dixon.py \

  --output_folder=$OUTPUT_FOLDER \

  --reference_header_nifti=$DIXON_NIFTI_DIR/mask.nii.gz \

  --save_what=prob \

  --threshold_method=fg \

  --fat=$DIXON_NIFTI_DIR/fat.nii.gz\

  --water=$DIXON_NIFTI_DIR/water.nii.gz\

  --inphase=$DIXON_NIFTI_DIR/ip.nii.gz\

  --outphase=$DIXON_NIFTI_DIR/op.nii.gz\

  --mask=$DIXON_NIFTI_DIR/mask.nii.gz\

  --restore_string=$RESTORE_STRING

```



### Pancreas 3D T1-weighted Segmentation

This command performs inference on dixon data. See `/tmp/ukbb_mri_sseg_output` for output

```

PANCREAS_MODEL_H5=trained_models/pancreas_t1w/20200104_shape-rep-a-dice2-ep50.h5

python3 public_sseg/infer_pancreas_t1w.py --action=infer \

    --output_folder=$OUTPUT_FOLDER \

    --IDS_FILE=example_ids_list.txt \

    --model_h5_path=$PANCREAS_MODEL_H5 \

    --input_nifti_subject_dir=$PROCESSED_NIFTI_DIR

```



### Liver IDEAL 2D Segmentation

This command performs inference on dixon data. See `/tmp/ukbb_mri_sseg_output` for output

```

LIVER_IDEAL_MODEL_H5=trained_models/liver_ideal/20191002_withphase_low_liver_2d_ideal.h5

python3 public_sseg/infer_liver_ideal_multiecho.py --action=infer \

    --output_folder=$OUTPUT_FOLDER \

    --data_modality=ideal_liver \

    --IDS_FILE=example_ids_list.txt \

    --model_h5_path=$LIVER_IDEAL_MODEL_H5 \

    --input_nifti_subject_dir=$PROCESSED_NIFTI_DIR

```



### Liver Multiecho 2D Segmentation

No worked example is available. The UKBB did release a reference resource file for this modality. If you already have the multiecho files downloaded and processed through the pipeline, and placed amongst the other nifti files inside `processed_nifti/ESS3TE4SJ3DEP5C5/nifti`, then command to segment the multiecho data would be:



```

LIVER_MULTIECHO_MODEL_H5=trained_models/liver_multiecho/20191002_withphase_low_liver_2d_multiecho.h5

python3 public_sseg/infer_liver_ideal_multiecho.py --action=infer \

    --output_folder=$OUTPUT_FOLDER \

    --data_modality=multiecho_liver \

    --IDS_FILE=example_ids_list.txt \

    --model_h5_path=$LIVER_IDEAL_MODEL_H5 \

    --input_nifti_subject_dir=$PROCESSED_NIFTI_DIR

```