Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields
This work addresses a domain-specific challenge in medical imaging by providing a more versatile tool for clinicians to visualize complex anatomical structures like vascular or bone systems, though it is incremental as it builds on existing unfolding and neural field techniques.
The paper tackles the problem of visualizing non-planar sparse anatomical structures in tomographic volumes by developing a neural field-based framework for flattening them into 2D representations with minimal distortion, achieving improved versatility and outperforming mesh-based baselines in peak distortion metrics.
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.