# Internal Organ Localization Using Depth Images

arXiv: https://arxiv.org/abs/2503.23468 · HTML: https://mlsanity.com/paper/2503.23468/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 30 Mar 2025.

## Builds on (most cited first, 3 of 3)

- [OSSO: Obtaining Skeletal Shape from Outside](https://mlsanity.com/paper/2204.10129/) (2022)
- [BOSS: Bones, Organs and Skin Shape Model](https://mlsanity.com/paper/2303.04923/) (2023)
- [Automated CT Lung Cancer Screening Workflow using 3D Camera](https://mlsanity.com/paper/2309.15750/) (2023)

## Built on by (most cited first, 1 of 1)

- [Depth to Anatomy: Organ Localization from Depth Images for Automated Patient Table Positioning in Radiology Workflow](https://mlsanity.com/paper/2601.18260/) (2026)

## Abstract (arXiv)

Automated patient positioning is a crucial step in streamlining MRI workflows and enhancing patient throughput. RGB-D camera-based systems offer a promising approach to automate this process by leveraging depth information to estimate internal organ positions. This paper investigates the feasibility of a learning-based framework to infer approximate internal organ positions from the body surface. Our approach utilizes a large-scale dataset of MRI scans to train a deep learning model capable of accurately predicting organ positions and shapes from depth images alone. We demonstrate the effectiveness of our method in localization of multiple internal organs, including bones and soft tissues. Our findings suggest that RGB-D camera-based systems integrated into MRI workflows have the potential to streamline scanning procedures and improve patient experience by enabling accurate and automated patient positioning.