Deep-learning cardiac motion analysis for human survival prediction

Research output: Contribution to journalArticle

Authors

  • Ghalib A. Bello
  • Timothy J. W. Dawes
  • Carlo Biffi
  • Antonio De Marvao
  • Luke S. G. E. Howard
  • J. Simon R. Gibbs
  • Martin R. Wilkins
  • Stuart A. Cook
  • Daniel Rueckert
  • Declan P. O’regan

Colleges, School and Institutes

Abstract

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimizing the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimized for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients, the predictive accuracy (quantified by Harrell’s C-index) was significantly higher (P = 0.0012) for our model C = 0.75 (95% CI: 0.70–0.79) than the human benchmark of C = 0.59 (95% CI: 0.53–0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.

Details

Original languageEnglish
Pages (from-to)95-104
Number of pages10
JournalNature Machine Intelligence
Volume1
Issue number2
Publication statusPublished - 11 Feb 2019