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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.18415 |
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| _version_ | 1866909406494457856 |
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| author | Rist, Leonhard Stephan, Pluvio Maul, Noah Vorberg, Linda Ditt, Hendrik Sühling, Michael Maier, Andreas Egger, Bernhard Taubmann, Oliver |
| author_facet | Rist, Leonhard Stephan, Pluvio Maul, Noah Vorberg, Linda Ditt, Hendrik Sühling, Michael Maier, Andreas Egger, Bernhard Taubmann, Oliver |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18415 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields Rist, Leonhard Stephan, Pluvio Maul, Noah Vorberg, Linda Ditt, Hendrik Sühling, Michael Maier, Andreas Egger, Bernhard Taubmann, Oliver Computer Vision and Pattern Recognition 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. |
| title | Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.18415 |