Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields

作     者：Rist, Leonhard Stephan, Pluvio Maul, Noah Vorberg, Linda Ditt, Hendrik Sühling, Michael Maier, Andreas Egger, Bernhard Taubmann, Oliver 

作者机构：Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Computed Tomography Siemens Healthineers AG Forchheim Germany Chair of Visual Computing Friedrich-Alexander-Universität Erlangen-Nürnberg Germany 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Medical imaging 

摘      要：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. Copyright © 2024, The Authors. All rights reserved.