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Stroke outcome and evolution prediction from CT brain using a spatiotemporal diffusion autoencoder

Adam Marcus, Paul Bentley, Daniel Rueckert

TL;DR

This work applies recent ideas in the field of diffusion probabilistic models to generate a self-supervised semantically meaningful stroke representation from Computed Tomography images, and improves this representation by extending the method to accommodate longitudinal images and the time from stroke onset.

Abstract

Stroke is a major cause of death and disability worldwide. Accurate outcome and evolution prediction has the potential to revolutionize stroke care by individualizing clinical decision-making leading to better outcomes. However, despite a plethora of attempts and the rich data provided by neuroimaging, modelling the ultimate fate of brain tissue remains a challenging task. In this work, we apply recent ideas in the field of diffusion probabilistic models to generate a self-supervised semantically meaningful stroke representation from Computed Tomography (CT) images. We then improve this representation by extending the method to accommodate longitudinal images and the time from stroke onset. The effectiveness of our approach is evaluated on a dataset consisting of 5,824 CT images from 3,573 patients across two medical centers with minimal labels. Comparative experiments show that our method achieves the best performance for predicting next-day severity and functional outcome at discharge.

Stroke outcome and evolution prediction from CT brain using a spatiotemporal diffusion autoencoder

TL;DR

This work applies recent ideas in the field of diffusion probabilistic models to generate a self-supervised semantically meaningful stroke representation from Computed Tomography images, and improves this representation by extending the method to accommodate longitudinal images and the time from stroke onset.

Abstract

Stroke is a major cause of death and disability worldwide. Accurate outcome and evolution prediction has the potential to revolutionize stroke care by individualizing clinical decision-making leading to better outcomes. However, despite a plethora of attempts and the rich data provided by neuroimaging, modelling the ultimate fate of brain tissue remains a challenging task. In this work, we apply recent ideas in the field of diffusion probabilistic models to generate a self-supervised semantically meaningful stroke representation from Computed Tomography (CT) images. We then improve this representation by extending the method to accommodate longitudinal images and the time from stroke onset. The effectiveness of our approach is evaluated on a dataset consisting of 5,824 CT images from 3,573 patients across two medical centers with minimal labels. Comparative experiments show that our method achieves the best performance for predicting next-day severity and functional outcome at discharge.
Paper Structure (17 sections, 3 equations, 2 figures, 3 tables)

This paper contains 17 sections, 3 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Contributions
  4. Method
  5. Spatial DDPM
  6. Spatiotemporal DDPM
  7. Experiments
  8. Materials
  9. Dataset
  10. Evaluation
  11. Implementation
  12. Results
  13. Comparison with Baseline
  14. Comparison with the Literature
  15. Ablation Study
  16. ...and 2 more sections

Figures (2)

  • Figure 1: Overview of the (A) spatial and (B) spatiotemporal diffusion autoencoder approaches. In both cases, a semantic encoder takes an image containing a stroke lesion and maps it to a latent code. A Denoising Diffusion Probabilistic Models (DDPM) ho2020denoising is then conditioned on this latent code to denoise a different image of the same lesion taken either at (A) the same time or (B) a future point in time. For the spatiotemporal method, the latent code is also concatenated with the future time using a multilayer perceptron (MLP). After training, the semantic encoder can then be fine-tuned with minimal data and used to predict a stroke outcome.
  • Figure 2: Example reconstructed image of a right middle cerebral artery (MCA) stroke from our test set for different methods. The performance of our spatiotemporal approach and the diffusion autoencoder (AE) are similar and superior to a variational AE.