Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields

• URL: http://arxiv.org/abs/2411.18415v1
• Date: Wed, 27 Nov 2024 14:58:49 GMT
• Title: Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields
• Authors: Leonhard Rist, Pluvio Stephan, Noah Maul, Linda Vorberg, Hendrik Ditt, Michael Sühling, Andreas Maier, Bernhard Egger, Oliver Taubmann,
• Abstract summary: Tomographic imaging reveals internal structures of 3D objects and is crucial for medical diagnoses. Various organ-specific unfolding techniques exist to map their densely sampled 3D surfaces to a distortion-minimized 2D representation. We deploy a neural field to fit the transformation of the anatomy of interest to a 2D overview image.
• Score: 6.5082099033254135
• License:
• 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.

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