Simulating Organogenesis: Algorithms for the Image-based Determination of Displacement Fields

TL;DR

This paper develops and evaluates landmark-free algorithms for determining displacement fields from 3D organ images, aiding simulations of organ development.

Contribution

It introduces and compares multiple algorithms for displacement field estimation, highlighting the effectiveness of the normal distance method and diffusion-based mapping.

Findings

Normal distance algorithm is most effective in most cases.

Diffusion-based mapping is a good alternative when normal distance fails.

Algorithms are tested on synthetic and real 2D and 3D data.

Abstract

Recent advances in imaging technology now provide us with 3D images of developing organs. These can be used to extract 3D geometries for simulations of organ development. To solve models on growing domains, the displacement fields between consecutive image frames need to be determined. Here we develop and evaluate different landmark-free algorithms for the determination of such displacement fields from image data. In particular, we examine minimal distance, normal distance, diffusion-based and uniform mapping algorithms and test these algorithms with both synthetic and real data in 2D and 3D. We conclude that in most cases the normal distance algorithm is the method of choice and wherever it fails, diffusion-based mapping provides a good alternative.