NODER: Image Sequence Regression Based on Neural Ordinary Differential Equations

TL;DR

NODER is a novel neural ODE-based framework for 3D medical image sequence regression that captures complex dynamics efficiently, outperforming existing methods especially with limited data.

Contribution

The paper introduces NODER, a neural ODE-based approach that models complex image sequence dynamics in latent space, reducing computational costs and improving performance over prior methods.

Findings

Achieves state-of-the-art 3D image regression performance.

Requires only a few images for accurate prediction.

Demonstrates effectiveness on ADNI and ACDC datasets.

Abstract

Regression on medical image sequences can capture temporal image pattern changes and predict images at missing or future time points. However, existing geodesic regression methods limit their regression performance by a strong underlying assumption of linear dynamics, while diffusion-based methods have high computational costs and lack constraints to preserve image topology. In this paper, we propose an optimization-based new framework called NODER, which leverages neural ordinary differential equations to capture complex underlying dynamics and reduces its high computational cost of handling high-dimensional image volumes by introducing the latent space. We compare our NODER with two recent regression methods, and the experimental results on ADNI and ACDC datasets demonstrate that our method achieves the state-of-the-art performance in 3D image regression. Our model needs only a couple of images in a sequence for prediction, which is practical, especially for clinical situations where extremely limited image time series are available for analysis. Our source code is available at https://github.com/ZedKing12138/NODER-pytorch.