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NOTE: THIS DESCRIPTION IS UNDER CONSTRUCTION

Unsupervised dynamic modeling of medical image transformations

Official implementation of Unsupervised dynamic modeling of medical image transformations accepted at 25th International Conference on Information Fusion 2022 (https://www.fusion2022.se/).

[Paper] [Presentation] [Dataset]

Enviroment

We use the following libraries

tensorflow 2.4.1
tensorflow-probability 0.12.1
numpy 1.19.5
cv2 4.5.1
pandas 1.2.2
tqdm 4.57.0
json 2.0.9
voxelmorph 0.1
SimpleITK 2.0.2
tqdm 4.56.0
matplotlib 3.3.4

Architecture

We model the motion by mapping high-dimensional sequential images to a low-dimensional space wherein a linear relationship between a hidden state process and the lower-dimensional representation holds. For this, we use a Conditional Variational Auto-Encoder (CVAE) to nonlinearly map the higher-dimensional images to a lower-dimensional space. In this lower-dimensional space, we model the dynamics with a Linear Gaussian State Space Model (LGSSM). Using the decoder we upsample the dynamics, from the low-dimensional space to the image space and estimate the spatial transformation between a reference image and the current. The image reconstruction can the be obtain by transform the reference image with the spatial transformation.

During training the CVAE - and LGSSM parameters are jointly updated by maximizing the evidence lower bound of the log likelihood:

Architecture

In the current implementation we use a image sequence of $50$ images $\mathbf{y} = [y_1, \dots, y_{50}]$ where $y_i \in R^{112 \times 112}$. The images was reduced using the encoder such that $x \in R^{16}$ and for state space variable $z$ we used $z \in R^{32}$.

Architecture Architecture

Fig.1 - Illustration and graphical representation of the model.

Run (TODO)

To train the model run

python train.py --dim_z 32 --gpus 1 --prefix Test --ds_path PATH_TO_DS

Following arguments is supported

--help            show this help message and exit
--dim_y           dimension of image variable (default (112,112))
--dim_x           dimension of latent variable (default 16)
--dim_z           dimension of state space variable (default 32)
--dec_in          input dim to decoder (default 16)
--saved_model     model path if continue running model (default:None)
--start_epoch     start epoch
--gpus            comma separated list of GPUs (default -1 (CPU))
--prefix          predix for log folder (default:None)
--ds_path         path to dataset (default:/data/Niklas/EchoNet-Dynamics)
--ds_size         Size of datasets

To test

import json
import numpy as np
from src.datasetLoader import TensorflowDatasetLoader

def get_config(path):
    with open('%s/%s' % (os.path.dirname(path), 'config.json')) as data_file:
        config_dict = json.load(data_file)
    config = namedtuple("Config", config_dict.keys())(*config_dict.values())
    return config, config_dict
    
path = './logs/kvae/32_kvae/32_Test/'
kvae_dir = path
kvae_config, kvae_config_dict = get_config(kvae_dir)
kvae_model = KVAE(config = kvae_config)
kvae_model.load_weights(kvae_dir + 'kvae_cardiac_32').expect_partial()

# Load validation data
val_generator = TensorflowDatasetLoader(root = PATH_TO_DS, image_shape = tuple(kvae_config.dim_y), length = kvae_config.ph_steps, split='val', period = kvae_config.period)

# Reconstruction 
val_batch = list(val_generator.data.take(1).as_numpy_iterator())[0]
inputs = [val_batch[0][None,...], val_batch[1][None,...]]
y_filt_val, y_smooth_val, y_vae_val = kvae_model.predict(inputs)

# Impute/predict
mask = np.ones(shape=50).astype('bool')
known = np.arange(50)
steps = 5
last = 1
known = known[:-last][::steps]
mask[known] = False
inputs = [val_batch[0][None,...], mask[None,...]]
y_filt_val, y_smooth_val, y_vae_val = kvae_model.predict(inputs)

# Latent space analysis
x_mu_smooth, x_cov_smooth, x_mu_filt, x_covs_filt, x_mu_filt_pred, x_covs_filt_pred = kvae_model.get_latents(inputs)

Result (TODO)

Smooth, filter and one-step-ahead prediction when all frames are known

steps1_seg_0.mp4

Smooth, filter and prediction when every 3rd frames are known

steps3_seg_0.mp4

Citation

@INPROCEEDINGS{9841369,
  author={Gunnarsson, Niklas and Sjölund, Jens and Kimstrand, Peter and Schön, Thomas B.},
  booktitle={2022 25th International Conference on Information Fusion (FUSION)}, 
  title={Unsupervised dynamic modeling of medical image transformations}, 
  year={2022},
  volume={},
  number={},
  pages={01-07},
  doi={10.23919/FUSION49751.2022.9841369}}