Bibliographic Details
| Title: |
Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields |
| Authors: |
Rist, Leonhard, Stephan, Pluvio, Maul, Noah, Vorberg, Linda, Ditt, Hendrik, Sühling, Michael, Maier, Andreas, Egger, Bernhard, Taubmann, Oliver |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computer Vision and Pattern Recognition |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2411.18415 |
| Accession Number: |
edsarx.2411.18415 |
| Database: |
arXiv |
