LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912339407667200 |
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| author | Henrich, Pit Mathis-Ullrich, Franziska |
| author_facet | Henrich, Pit Mathis-Ullrich, Franziska |
| contents | We introduce a novel approach for the precise localization of 67 anatomical structures from single depth images captured from the exterior of the human body. Our method uses a multi-class occupancy network, trained using segmented CT scans augmented with body-pose changes, and incorporates a specialized sampling strategy to handle densely packed internal organs. Our contributions include the application of occupancy networks for occluded structure localization, a robust method for estimating anatomical positions from depth images, and the creation of detailed, individualized 3D anatomical atlases. We outperform localization using template matching and provide qualitative real-world reconstructions. This method promises improvements in automated medical imaging and diagnostic procedures by offering accurate, non-invasive localization of critical anatomical structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12407 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images Henrich, Pit Mathis-Ullrich, Franziska Computer Vision and Pattern Recognition We introduce a novel approach for the precise localization of 67 anatomical structures from single depth images captured from the exterior of the human body. Our method uses a multi-class occupancy network, trained using segmented CT scans augmented with body-pose changes, and incorporates a specialized sampling strategy to handle densely packed internal organs. Our contributions include the application of occupancy networks for occluded structure localization, a robust method for estimating anatomical positions from depth images, and the creation of detailed, individualized 3D anatomical atlases. We outperform localization using template matching and provide qualitative real-world reconstructions. This method promises improvements in automated medical imaging and diagnostic procedures by offering accurate, non-invasive localization of critical anatomical structures. |
| title | LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.12407 |