Depth to Anatomy: Learning Internal Organ Locations from Surface Depth Images

Automated patient positioning plays an important role in optimizing scanning procedure and improving patient throughput. Leveraging depth information captured by RGB-D cameras presents a promising approach for estimating internal organ positions, thereby enabling more accurate and efficient positioning. In this work, we propose a learning-based framework that directly predicts the 3D locations and shapes of multiple internal organs from single 2D depth images of the body surface. Utilizing a large-scale dataset of full-body MRI scans, we synthesize depth images paired with corresponding anatomical segmentations to train a unified convolutional neural network architecture. Our method accurately localizes a diverse set of anatomical structures, including bones and soft tissues, without requiring explicit surface reconstruction. Experimental results demonstrate the potential of integrating depth sensors into radiology workflows to streamline scanning procedures and enhance patient experience through automated patient positioning.

Internal Organ Localization Using Depth Images

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.