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

• URL: http://arxiv.org/abs/2406.12407v2
• Date: Tue, 22 Apr 2025 08:07:41 GMT
• Title: LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images
• Authors: Pit Henrich, Franziska Mathis-Ullrich,
• Abstract summary: 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.<n>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.
• Score: 2.104687387907779
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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