A metric to compare the anatomy variation between image time series

TL;DR

This paper introduces a generalized Fréchet distance-based metric to compare anatomical variations in image time series, effectively separating path and shape differences in biological processes.

Contribution

It presents a novel metric for comparing biological image time series that disentangles path evolution from anatomical shape differences.

Findings

Successfully separates path and shape differences in simulated data.

Effectively quantifies differences in adult and fetal neuro templates.

Demonstrates robustness in real biological data.

Abstract

Biological processes like growth, aging, and disease progression are generally studied with follow-up scans taken at different time points, i.e., with image time series (TS) based analysis. Comparison between TS representing a biological process of two individuals/populations is of interest. A metric to quantify the difference between TS is desirable for such a comparison. The two TS represent the evolution of two different subject/population average anatomies through two paths. A method to untangle and quantify the path and inter-subject anatomy(shape) difference between the TS is presented in this paper. The proposed metric is a generalized version of Fr\'echet distance designed to compare curves. The proposed method is evaluated with simulated and adult and fetal neuro templates. Results show that the metric is able to separate and quantify the path and shape differences between TS.