Internal Organ Localization Using Depth Images
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908290352414720 |
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| author | Kats, Eytan Geißler, Kai Hirsch, Jochen G. Heldman, Stefan Heinrich, Mattias P. |
| author_facet | Kats, Eytan Geißler, Kai Hirsch, Jochen G. Heldman, Stefan Heinrich, Mattias P. |
| contents | Automated patient positioning is a crucial step in streamlining MRI workflows and enhancing patient throughput. RGB-D camera-based systems offer a promising approach to automate this process by leveraging depth information to estimate internal organ positions. This paper investigates the feasibility of a learning-based framework to infer approximate internal organ positions from the body surface. Our approach utilizes a large-scale dataset of MRI scans to train a deep learning model capable of accurately predicting organ positions and shapes from depth images alone. We demonstrate the effectiveness of our method in localization of multiple internal organs, including bones and soft tissues. Our findings suggest that RGB-D camera-based systems integrated into MRI workflows have the potential to streamline scanning procedures and improve patient experience by enabling accurate and automated patient positioning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23468 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Internal Organ Localization Using Depth Images Kats, Eytan Geißler, Kai Hirsch, Jochen G. Heldman, Stefan Heinrich, Mattias P. Computer Vision and Pattern Recognition Automated patient positioning is a crucial step in streamlining MRI workflows and enhancing patient throughput. RGB-D camera-based systems offer a promising approach to automate this process by leveraging depth information to estimate internal organ positions. This paper investigates the feasibility of a learning-based framework to infer approximate internal organ positions from the body surface. Our approach utilizes a large-scale dataset of MRI scans to train a deep learning model capable of accurately predicting organ positions and shapes from depth images alone. We demonstrate the effectiveness of our method in localization of multiple internal organs, including bones and soft tissues. Our findings suggest that RGB-D camera-based systems integrated into MRI workflows have the potential to streamline scanning procedures and improve patient experience by enabling accurate and automated patient positioning. |
| title | Internal Organ Localization Using Depth Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.23468 |