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

TL;DR

This paper presents a novel deep learning approach using occupancy networks to accurately localize 67 internal organs from single exterior depth images, enhancing non-invasive medical imaging.

Contribution

It introduces the application of occupancy networks for internal organ localization from depth images and develops detailed 3D anatomical atlases for improved diagnostics.

Findings

Outperforms template matching in localization accuracy

Provides qualitative real-world reconstructions

Offers a robust method for anatomical position estimation

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.