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

Henrich P, Mathis-Ullrich F (2025)

Publication Type: Journal article

Publication year: 2025

Journal

IEEE Access Institute of Electrical and Electronics Engineers (IEEE)

DOI: 10.1109/ACCESS.2025.3543736

Abstract

We introduce a novel approach employing occupancy networks for the precise localization of 67 anatomical structures from single depth images captured from the exterior of the human body. This approach considers the anatomical diversity across individuals. 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 medical imaging and automated diagnostic procedures by offering accurate, non-invasive localization of critical anatomical features.

Authors with CRIS profile

Pit Henrich Professur für Robotische Planung und Kognition in der Chirurgie Franziska Mathis-Ullrich Professur für Robotische Planung und Kognition in der Chirurgie

How to cite

APA:

Henrich, P., & Mathis-Ullrich, F. (2025). LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3543736

MLA:

Henrich, Pit, and Franziska Mathis-Ullrich. "LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images." IEEE Access (2025).

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