LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images

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.

@article{henrich2025_2406.12407,
  title={ LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images },
  author={ Pit Henrich and Franziska Mathis-Ullrich },
  journal={arXiv preprint arXiv:2406.12407},
  year={ 2025 }
}