Tomographic imaging reveals internal structures of 3D objects and is crucial for medical diagnoses. Visualizing the morphology and appearance of non-planar sparse anatomical structures that extend over multiple 2D slices in tomographic volumes is inherently difficult but valuable for decision-making and reporting. Hence, various organ-specific unfolding techniques exist to map their densely sampled 3D surfaces to a distortion-minimized 2D representation. However, there is no versatile framework to flatten complex sparse structures including vascular, duct or bone systems. We deploy a neural field to fit the transformation of the anatomy of interest to a 2D overview image. We further propose distortion regularization strategies and combine geometric with intensity-based loss formulations to also display non-annotated and auxiliary targets. In addition to improved versatility, our unfolding technique outperforms mesh-based baselines for sparse structures w.r.t. peak distortion and our regularization scheme yields smoother transformations compared to Jacobian formulations from neural field-based image registration.
@article{arxiv.2411.18415,
title = {Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields},
author = {Leonhard Rist and Pluvio Stephan and Noah Maul and Linda Vorberg and Hendrik Ditt and Michael Sühling and Andreas Maier and Bernhard Egger and Oliver Taubmann},
journal= {arXiv preprint arXiv:2411.18415},
year = {2024}
}