Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields

TL;DR

This paper introduces a neural field-based framework for unfolding complex sparse anatomical structures in tomographic images, improving versatility and reducing distortion compared to traditional mesh-based methods.

Contribution

It presents a novel neural field approach with regularization strategies for flattening complex anatomical structures, outperforming existing mesh-based baselines.

Findings

Outperforms mesh-based baselines in peak distortion

Provides smoother transformations than Jacobian-based registration

Handles non-annotated and auxiliary targets effectively

Abstract

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.