Longitudinal studies of infants' brains are essential for research and clinical detection of Neurodevelopmental Disorders. However, for infant brain MRI scans, effective deep learning-based segmentation frameworks exist only within small age intervals due the large image intensity and contrast changes that take place in the early postnatal stages of development. However, using different segmentation frameworks or models at different age intervals within the same longitudinal data set would cause segmentation inconsistencies and age-specific biases. Thus, an age-agnostic segmentation model for infants' brains is needed. In this paper, we present "Infant-SynthSeg", an extension of the contrast-agnostic SynthSeg segmentation framework applicable to MRI data of infant at ages within the first year of life. Our work mainly focuses on extending learning strategies related to synthetic data generation and augmentation, with the aim of creating a method that employs training data capturing features unique to infants' brains during this early-stage development. Comparison across different learning strategy settings, as well as a more-traditional contrast-aware deep learning model (NN-Unet) are presented. Our experiments show that our trained Infant-SynthSeg models show consistently high segmentation performance on MRI scans of infant brains throughout the first year of life. Furthermore, as the model is trained on ground truth labels at different ages, even labels that are not present at certain ages (such as cerebellar white matter at 1 month) can be appropriately segmented via Infant-SynthSeg across the whole age range. Finally, while Infant-SynthSeg shows consistent segmentation performance across the first year of life, it is outperformed by age-specific deep learning models trained for a specific narrow age range.
Please put in all the training paremeters/resource directories/configurations in scripts/niral_scripts/train_infant.py (additional instructions are provided inside the file) and run
python3 -m scripts.niral_scripts.train_infant
Our training infant label maps are in data/infant_synthseg_training/training_labels.
Our T1 priors are in data/infant_synthseg_training/T1merged.
Our T2 priors are in data/infant_synthseg_training/T2merged.
Please put in the file paths and list of labels to generate in scripts/niral_scripts/intensity_estimation_infant, then run
python3 -m scripts.niral_scripts.intensity_estimation_infant
Apply a unet model on your data:
python3 -m scripts.niral_scripts.predict_infant [grayscale images dir] [dir to save the segmentations] [path to the .h5 model file] --out_posteriors [dir for saving the posterior probabilities]
The models we trained are at models/T1.h5 and models/T2.h5 If you are using your own trained model, please update "segmentation_label_list" to what you have trained on at scripts/niral_scripts/predict_infant
python3 -m scripts.niral_scripts.merge_infant [T1 segmentations dir] [T1 posterior dir] [T2 segmentation dir] [T2 posterior dir] [dir to save the merged segmantations] [dir to save the merged posteriors]
Please put in the required resource/saving directories and label list in scripts/niral_scripts/island_processing and run:
python3 -m scripts.niral_scripts.island_processing
The newly added fuse_classes() in scripts/niral_scripts/train_infant.py that automates the process generating generation_classes for fused labels might have issues and need more thorough testing. In that case, manually creating the generation_classes may be needed. (an example would be in scripts/niral_scripts/generate_images_infant).
This project is based on the SynthSeg framework, where additional tutorials could also be found.
A Learning Strategy for Contrast-agnostic MRI Segmentation
Benjamin Billot, Douglas N. Greve, Koen Van Leemput, Bruce Fischl, Juan Eugenio Iglesias*, Adrian V. Dalca*
*contributed equally
MIDL 2020
[link | arxiv | bibtex]
Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast
Benjamin Billot, Eleanor D. Robinson, Adrian V. Dalca, Juan Eugenio Iglesias
MICCAI 2020
[link | arxiv | bibtex]