Progressive Subsampling for Oversampled Data - Application to Quantitative MRI

This repository is the official implementation of our paper accepted in: Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 MICCAI version and arxiv version.

Please consider citing:

@article{blumberg2022,
    author={Stefano B. Blumberg and Hongxiang Lin and Francesco Grussu and Yukun Zhou and Matteo Figini and Daniel C. Alexander},
    title={Progressive Subsampling for Oversampled Data - Application to Quantitative {MRI}},
    journal={Medical Image Computing and Computer Assisted Intervention (MICCAI)},
    year = {2022}
}

Requirements

Base Installation

For the GPU version:

$ conda create -n PROSUB python=3.6.8 # Optional create new conda environment
$ . activate PROSUB # Optional activate conda environment
$ conda install -c conda-forge cudatoolkit=9.2 cudnn=7.6.5 # For tensorflow GPU, change values of cuda and cudnn
$ pip install -r requirements.txt # May have to add -I flag depending on system

Installing AutoKeras

Install autokeras from source version 1.0.16.post1 and modify few lines.

$ git clone https://github.com/keras-team/autokeras.git --branch 1.0.16.post1 --single-branch
$ cd autokeras; python setup.py install; cd ..
$ git apply autokeras_diff.patch

Our Configuration

• CentOS 8
• cuda 10.1
• cudnn 7.6.5
• Titan V GPU

Data Preparation

To use MUDI data from paper:

• Register and download link
• Create a dictionary of numpy arrays, with keys: '0011' as np.array no_samplesx1344 data from subject 11 et.c

Example to create dummy data:

import numpy as np; import pickle
no_samples=1000
no_measurements=1344
data_keys = ('0011','0012','0013','0014','0015')
data = {subj: np.random.rand(no_samples,no_measurements) for subj in data_keys}
with open('data.pkl','wb') as f:  pickle.dump(data, f)

Results

Main results from Table 1 in the paper.

Whole brain Mean-Squared-Error between $ N=1344 $ reconstructed measurements and $ N $ ground-truth measurements, on leave-one-out cross validation on five MUlti-DIffusion (MUDI) challenge subjects. The SARDU-Net won the MUDI challenge.

Model	M = 500	250	100	50	40	30	20	10
SARDU-Net-v1	1.45 +-0.14	1.72 +-0.15	4.73 +- 0.57	5.15 +- 0.63	6.10 +- 0.79	21.0 +- 6.07	19.8 +- 9.26	22.8 +- 6.57
SARDU-Net-v2	0.88 +- 0.10	0.89 +- 0.01	1.36 +- 0.14	1.66 +- 0.10	1.95 +- 0.12	2.27 +- 0.20	3.01 +- 0.45	4.41 +- 1.39
SARDU-Net-v2-BOF	0.83 +- 0.10	0.86 +- 0.10	1.30 +- 0.12	1.67 +- 0.12	1.86 +- 0.18	2.15 +- 0.23	2.61 +- 0.24	3.74 +- 0.66
SARDU-Net-v2-NAS	0.82 +- 0.13	0.99 +- 0.12	1.34 +- 0.26	1.76 +- 0.24	2.23 +- 0.22	6.00 +- 7.14	2.82 +- 0.41	4.27 +- 1.66
PROSUB	0.49 +- 0.07	0.61 +- 0.11	0.89 +- 0.11	1.35 +- 0.11	1.53 +- 0.05	1.87 +- 0.19	2.50 +- 0.40	3.48 +- 0.55

Training and Evaluation for Results

To replicate the results:

SARDU-Net (Baselines)

For SARDU-Net-v1, SARDU-Net-v2, SARDU-Net-v2-BOF, use the original SARDU-Net code.

SARDU-Net-v2-NAS (Baseline)

# Final result is the average across the splits i.e. across SPLIT_NO

# Replace with your variables used in main.py:
data_fil=<ADD>
out_base=<ADD>

# Choose one of the two below:
#num_units_init='1063 781 781 1063' # for M=500
num_units_init='417 333 781 1063' # for M<500

T_values='4 8 12 16 20 24 28 32 36'
M_values='1344 500 250 100 50 40 30 20 10'

for SPLIT_NO in {0..4}
do
    case $SPLIT_NO in
        0) data_train_subjs="0012 0013 0014"; data_val_subjs="0015"; data_test_subjs="0011"; split_name="0011out";;
        1) data_train_subjs="0013 0014 0015"; data_val_subjs="0011"; data_test_subjs="0012"; split_name="0012out";;
        2) data_train_subjs="0014 0015 0011"; data_val_subjs="0012"; data_test_subjs="0013"; split_name="0013out";;
        3) data_train_subjs="0015 0011 0012"; data_val_subjs="0013"; data_test_subjs="0014"; split_name="0014out";;
        4) data_train_subjs="0011 0012 0013"; data_val_subjs="0014"; data_test_subjs="0015"; split_name="0015out";;
    esac

    python main.py \
        --data_fil $data_fil \
        --data_train_subjs $data_train_subjs \
        --data_val_subjs $data_val_subjs \
        --data_test_subjs $data_test_subjs \
        --proj_name SARDUNet-v2-NAS_run \
        --out_base $out_base \
        --seed 0 \
        --T_values $T_values \
        --M_values $M_values \
        --noepoch 200 \
        --epochs_decay 20 \
        --tuner greedy \
        --num_layers_nas 1 2 3 \
        --network_name sardunet-nas \
        --dropout_nas 0.2 0.0 0.1 0.3 0.4 \
        --data_normalization original \
        --num_units_nas {128..2048..128} \
        --num_units_init $num_units_init
done

PROSUB (Ours)

# Final result is the average across the splits i.e. across SPLIT_NO

# Replace with your variables used in main.py:
data_fil=<ADD>
out_base=<ADD>

T_values='4 8 12 16 20 24 28 32 36'
M_values='1344 500 250 100 50 40 30 20 10'
num_units_init='1063 781 781 1063'

# Cross-validation splits
for SPLIT_NO in {0..4}
do
    case $SPLIT_NO in
        0) data_train_subjs="0012 0013 0014"; data_val_subjs="0015"; data_test_subjs="0011"; split_name="0011out";;
        1) data_train_subjs="0013 0014 0015"; data_val_subjs="0011"; data_test_subjs="0012"; split_name="0012out";;
        2) data_train_subjs="0014 0015 0011"; data_val_subjs="0012"; data_test_subjs="0013"; split_name="0013out";;
        3) data_train_subjs="0015 0011 0012"; data_val_subjs="0013"; data_test_subjs="0014"; split_name="0014out";;
        4) data_train_subjs="0011 0012 0013"; data_val_subjs="0014"; data_test_subjs="0015"; split_name="0015out";;
    esac

    python main.py \
        --data_fil $data_fil \
        --data_train_subjs $data_train_subjs \
        --data_val_subjs $data_val_subjs \
        --data_test_subjs $data_test_subjs \
        --proj_name PROSUB_run \
        --out_base $out_base \
        --seed 0 \
        --T_values $T_values \
        --M_values $M_values \
        --noepoch 200 \
        --epochs_decay 20 \
        --tuner greedy \
        --num_layers_nas 1 2 3 \
        --network_name prosub \
        --dropout_nas 0.0 \
        --data_normalization original-measurement \
        --num_units_nas {128..2048..128} \
        --num_units_init $num_units_init
done

Contact and Feedback

stefano.blumberg.17@ucl.ac.uk