Skip to content

Latest commit

 

History

History
188 lines (134 loc) · 6.42 KB

README.md

File metadata and controls

188 lines (134 loc) · 6.42 KB
Raw

[MICCAI 2024] PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI

Peirong Liu1, Oula Puonti1, Annabel Sorby-Adams1, W. Taylor Kimberly1, Juan E. Iglesias1,2,3

1Harvard Medical School and Massachusetts General Hospital
2University College London    3Massachusetts Institute of Technology

drawing

Environment

Training and evaluation environment: Python 3.11.4, PyTorch 2.0.1, CUDA 12.2. Run the following command to install required packages.

conda create -n pepsi python=3.11
conda activate pepsi

cd /path/to/pepsi
pip install -r requirements.txt

Demo

Playing with PEPSI Anatomy & Pathology Image Synthesizer and Feature Extractor

Please download PEPSI pre-trained weights (pepsi_pretrained.pth), and test images with pathologies (T1w.nii.gz, FLAIR.nii.gz) in this Google Drive folder, and move them into the './assets' folder.

Obtain PEPSI synthesized MP-RAGE & FLAIR & features using the following code.

import os, torch
from utils.demo_utils import prepare_image, evaluate_image
from utils.misc import viewVolume, make_dir

img_path = 'assets/T1w.nii.gz' # Try: assets/T1w.nii.gz, assets/FLAIR.nii.gz
ckp_path = 'assets/pepsi_pretrained.pth'


im, aff = prepare_image(img_path, device = 'cuda:0')
outputs = evaluate_image(im, ckp_path, feature_only = False, device = 'cuda:0')


# Check outputs
for k in outputs.keys(): 
    print('out:', k)


# Get PEPSI synthesized MP-RAGE (Anatomy Image with Darkened Anomalies)
mprage = outputs['image']
print(mprage.size()) # (1, 1, h, w, d)
viewVolume(mprage, aff, names = ['out_mprage_from_%s' % os.path.basename(img_path).split('.nii.gz')[0]], save_dir = make_dir('outs'))

# Get PEPSI synthesized FLAIR (Pathology Image with Brightened Anomalies)
flair = outputs['aux_image']
print(flair.size()) # (1, 1, h, w, d)
viewVolume(flair, aff, names = ['out_flair_from_%s' % os.path.basename(img_path).split('.nii.gz')[0]], save_dir = make_dir('outs'))


# Get PEPSI features
feats = outputs['feat'][-1]
print(feats.size()) # (1, 64, h, w, d)
# Uncomment the following if you want to save the features
# NOTE: feature size could be large
#num_plot_feats = 1 # 64 features in total from the last layer
#for i in range(num_plot_feats): 
#  viewVolume(feats[:, i], aff, names = ['feat-%d' % (i+1)], save_dir = make_dir('outs/feats-%s' % os.path.basename(img_path).split('.nii.gz')[0]))

You could customize your own paths in scripts/demo_pepsi.py.

cd /path/to/pepsi
python scripts/demo_pepsi.py

Playing with PEPSI Synthetic Generator

cd /path/to/pepsi
python scripts/demo_synth.py

You could customize your own data generator in cfgs/demo_synth.yaml.

Training on Synthetic Data Encoded with Pathology

drawing

Use the following code to train a feature representation model on synthetic data:

cd /path/to/pepsi
python scripts/train.py anat_flair.yaml

We also support Slurm submission:

cd /path/to/pepsi
sbatch scripts/train.sh

You could customize your anatomy supervision by changing the configure file cfgs/train/anat_flair.yaml.

Evaluating on Real Data

Use the following code to fine-tune a task-specific pathology segmentation model on real data, using PEPSI pre-trained weights:

cd /path/to/pepsi
python scripts/eval.py task_seg.yaml

We also support Slurm submission:

cd /path/to/pepsi
sbatch scripts/eval.sh

You could customize your own task by creating your own cfgs/train/task_seg.yaml file.

Download the Public Datasets

  • PEPSI pre-trained model and test images: Google Drive

  • ATLAS dataset: Request data from official website.

  • ISLES2022 dataset: Request data from official website.

  • ADNI3 dataset: Request data from official website.

  • ISBI2015 dataset: Request data from official website.

  • MSSEG2016 dataset: Request data from official website.

  • Segmentation labels for data simulation: To train a PEPSI feature representation model of your own from scratch, one needs the segmentation labels for synthetic image simulation and their corresponding MP-RAGE/FLAIR images for dual-guidance (anatomy + pathology) supervision. We obtained our anatomy labels via three steps: (1) Inpaint the lesions: SynthSR toolbox; (2) Skull-strip: SynthStrip toolbox; (3) Obtain anatomy segmentation labels: SynthSeg toolbox. Please refer to the pre-processing steps in preprocess/generation_labels.py.

For obtaining the anatomy generation labels, we rely on FreeSurfer. Please install FreeSurfer first and activate the environment before running preprocess/generation_labels.py.

Datasets

After downloading the datasets needed, structure the data as follows, and set up your dataset paths in PEPSI/datasets/__init__.py.

/path/to/dataset/
  T1/
    subject_name.nii
    ...
  T2/
    subject_name.nii
    ...
  FLAIR/
    subject_name.nii
    ... 
  or_any_other_modality_you_have/
    subject_name.nii
    ...
    
  label_maps_segmentation/
    subject_name.nii
    ...
  label_maps_generation/
    subject_name.nii
    ...
    
  pathology_maps/
    subject_name.nii
    ...

Citation

@InProceedings{Liu_2024_PEPSI,
    author    = {Liu, Peirong and Puonti, Oula and Sorby-Adams, Annabel and Kimberly, W. Taylor and Iglesias, Juan E.},
    title     = {{PEPSI}: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain {MRI}},
    booktitle = {International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)},
    year      = {2024},
}