Longitudinal Representation Learning

Reference implementation of "Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis", to appear, NeurIPS 22 (oral).

Given longitudinal neuroimages with scarce annotation, pretrain a image-to-image network (i.e., U-Net) with the proposed self-supervised spatiotemporal representation learning objectives, and finetune with limited annotation (i.e. 1 subject atlas), optionally with a longitudinal consistency-regularization term.

Prerequisites

Linux (tested with Ubuntu 20.01)

Python 3 (anaconda)

CPU or NVIDIA GPU + CUDA CuDNN

PyTorch (tested with 1.10)

Dependencies

To install the environment, run

conda env create -f environment.yml

Data preparation

HDF5 is recommended for faster data loading of large volumetric 3D biomedical images.

For Longitudinal pretraining As we perform intra-subject sampling, the pretraining data loader requires subject-specific data structure. The h5 file is hierachically indexed by subject-id, and for each subject group, multiple (N) longitudinal acquisitions are stacked as a 4D array of shape [N, w, h, d]. Example code to generate such h5 file is given below:

import h5py
import numpy as np

long_train = h5py.File('./example_dataset/train_long.hdf5','w')
grp = long_train.create_group('subj1')

place_holder_t1=np.random.rand(3,128,160,160)  # 3 timepoints for subj1 
long_train['subj1']['t1']=place_holder_t1

place_holder_age=np.random.rand(3,1)
long_train['subj1']['age']=place_holder_age

long_train.close()

For segmetation finetuning The finetuning is done with cross-sectional image/annotation pairs which does not require subject-specific data loading. The h5 file could be generated as image-segmentation pairs as follows:

sup_train = h5py.File('./example_dataset/train_image_seg_3d.hdf5','w')

place_holder_t1=np.random.rand(3,128,160,160)  # 3 total images for supervised training
place_holder_seg = np.random.randint(33, size=place_holder_t1.shape).astype(float)
place_holder_age=np.random.rand(3,1)

grp = sup_train.create_group('img_seg_pair')

grp['t1']=place_holder_t1
grp['seg']=place_holder_seg
grp['age']=palce_holder_age
sup_train.close()

Pretraining

Run the following command to pretrain the model:

cd src/scripts
bash script_pretrain.sh

Finetuning

Without consistency regularization Make sure the pretrained model name is correctly specified in the script, and run the following command to finetune the model without segmentation consistency loss:

cd src/scripts
bash script_finetune.sh

With consistency regularization

cd src/scripts
bash script_finetune_w_Lcs.sh

Reference

This code base architecure is highly motivated by CUT. We refer to SimSiam for our projector and predictor implementation, and VICReg for the variance and covariance regularization terms.

Citation

If you use this codebase, please consider citation of our work:

@misc{https://doi.org/10.48550/arxiv.2206.04281,
  doi = {10.48550/ARXIV.2206.04281},
  url = {https://arxiv.org/abs/2206.04281},
  author = {Ren, Mengwei and Dey, Neel and Styner, Martin A. and Botteron, Kelly and Gerig, Guido},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

sunglyoungKim/longitudinal-representation-learning