Depth to Anatomy: Organ Localization from Depth Images for Automated Patient Table Positioning in Radiology Workflow
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917340128477184 |
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| author | Kats, Eytan Geissler, Kai Mensing, Daniel Senegas, Julien Hirsch, Jochen G. Heldman, Stefan Heinrich, Mattias P. |
| author_facet | Kats, Eytan Geissler, Kai Mensing, Daniel Senegas, Julien Hirsch, Jochen G. Heldman, Stefan Heinrich, Mattias P. |
| contents | Automated patient positioning can improve radiology workflow efficiency by reducing the time required for manual table adjustments and scout-based scan planning. We propose a learning-based framework that predicts 3D organ locations and shapes for 41 anatomical structures, including both bones and soft tissues, directly from a single 2D depth image of the body surface. Leveraging $10,020$ whole-body MRI scans from the German National Cohort (NAKO) dataset, we synthetically generate depth images paired with anatomical segmentations to train a convolutional neural network for volumetric organ prediction. Our method achieves a mean dice similarity coefficient of $0.44\pm0.2$ and and a symmetric average surface distance of $7.69\pm5.68$ mm across all structures. Furthermore, the model derives organ bounding boxes with a mean absolute detection offset of $10.99\pm5.54$ mm. Qualitative results on real-world depth images confirm the ability of the model to generalize to practical clinical settings. These findings suggest that depth-only organ localization can support automated patient positioning reducing setup time, minimizing operator variability, and improving patient comfort. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_18260 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Depth to Anatomy: Organ Localization from Depth Images for Automated Patient Table Positioning in Radiology Workflow Kats, Eytan Geissler, Kai Mensing, Daniel Senegas, Julien Hirsch, Jochen G. Heldman, Stefan Heinrich, Mattias P. Computer Vision and Pattern Recognition Automated patient positioning can improve radiology workflow efficiency by reducing the time required for manual table adjustments and scout-based scan planning. We propose a learning-based framework that predicts 3D organ locations and shapes for 41 anatomical structures, including both bones and soft tissues, directly from a single 2D depth image of the body surface. Leveraging $10,020$ whole-body MRI scans from the German National Cohort (NAKO) dataset, we synthetically generate depth images paired with anatomical segmentations to train a convolutional neural network for volumetric organ prediction. Our method achieves a mean dice similarity coefficient of $0.44\pm0.2$ and and a symmetric average surface distance of $7.69\pm5.68$ mm across all structures. Furthermore, the model derives organ bounding boxes with a mean absolute detection offset of $10.99\pm5.54$ mm. Qualitative results on real-world depth images confirm the ability of the model to generalize to practical clinical settings. These findings suggest that depth-only organ localization can support automated patient positioning reducing setup time, minimizing operator variability, and improving patient comfort. |
| title | Depth to Anatomy: Organ Localization from Depth Images for Automated Patient Table Positioning in Radiology Workflow |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.18260 |