LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images
This work addresses the need for accurate, non-invasive localization of internal organs in medical imaging and diagnostics, representing a domain-specific advancement.
The paper tackles the problem of precisely localizing 67 anatomical structures from single depth images of the human body exterior, achieving results that outperform template matching methods.
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.