TAAL

Test-time Augmentation for Active Learning

A semi-supervised AL method identifying the most uncertain samples based on applied transformations.

The results were published in MICCAI_DALI 2022 workshop.

Results

Results

Model performance across different AL cycles (increasinig number of labeled samples, each identififed by given AL strategy).

Data

Data folder has structure:

data
├── ACDC 
│ └── patient001 
│   └── Info.cfg 
│   └── patient001_4d.nii.gz 
│   └── patient001_frameXX.nii.gz 
│   └── patient001_frameXX_gt.nii.gz 
│   └── ...
│ └── ...
│ └── patient100 
│   └── Info.cfg 
│   └── patient001_4d.nii.gz 
│   └── patient001_frameXX.nii.gz 
│   └── patient001_frameXX_gt.nii.gz 
│   └── ..

Data taken from:

• ACDC Segmentation Challenge (100 samples) (2D): https://acdc.creatis.insa-lyon.fr/
  

Data Preprocessing

ACDC dataset contains 3D MRI data from 100 patients.
The shapes of these volumes vary between [154, 428] in the x-direction, [154, 512] in the y-direction and [6, 18] in the z-direction.

We take the data for diastolic (ED) cardiac phase.

1. Select phase
   We take the data for diastolic (ED) cardiac phase

2. Resample
   We center the image and resample the spacing to 1.0 in the x- and y-direction. The new image width is 256 x 256. Spacing and length in the z-direction are kept the same.

3. Normalize
   We apply 99th percentile normalization.

4. Convert to 2D
   We convert 3D volumes for each patient to 6-18 independent 2D slices. The slices are taken in the z-direction.

Running Preprocessing (jupyter notebook): ACDC-preprocessing.ipynb

Choose:

• num_test_patients = 20
• new_img_width = 256
• new_spacing = 1.

This will randomly select 20 patients to be used for testing. Each slice is processed in order.

The preprocessed training data is saved in: /data/preprocessed_ACDC_256_train_normalized. The preprocessed test data is saved in: /data/preprocessed_ACDC_256_test_normalized.

Here, patient ID list for testing is [81, 14, 3, 94, 35, 31, 28, 17, 13, 86, 69, 11, 75, 54, 4, 27, 29, 64, 77, 71].
We create one file in each preprocessed data folder called train_indices.txt or test_indices.txt. It contains 2 list with test patient ID's (train indices or test indices) and patient ID corresponding to each of the 191 test slices (train_volume_list or test_volume_list) (used to compute the metric on the 3D volume). Each folder also contains a file called train_position_indices.txt or test_positoni_indices.txt which contain a list of the relative position of each slice in its corresponding volume.

data
├── preprocessed_ACDC_256_train
│   └── data 
│   └── label 
|   └── train_indices.txt
|   └── train_position_indices.txt
├── preprocessed_ACDC_256_test
│   └── data 
│   └── label 
|   └── test_indices.txt
|   └── test_position_indices.txt

Models

1) Fully-supervised

• solver (fully supervised):

2) Semi-supervised

Training the model

Running Training: python main_train.py --device cuda:0 --config train_config

train_config.json contains information on run name, training parameters and params on dataset.

** Note that `Configs.configs' must be modified to add paths**

Metrics

Dice Score: F1 score measuring the harmonic mean of precision and recall. It is a per-pixel detection score.