Deep learning cardiac motion analysis for human survival prediction

TL;DR

This paper presents a deep learning framework that analyzes cardiac motion from MRI sequences to predict human survival, outperforming human benchmarks with high accuracy.

Contribution

The study introduces a novel hybrid neural network combining 3D motion segmentation and a task-specific autoencoder for survival prediction from cardiac MRI data.

Findings

Model achieved a C-index of 0.73, significantly higher than human benchmark of 0.59.

Dense motion modeling improved the accuracy of survival predictions.

The approach effectively handles high-dimensional medical imaging data for prognostic tasks.

Abstract

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising 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 optimised 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 < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) 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.