Depth to Anatomy: Organ Localization from Depth Images for Automated Patient Table Positioning in Radiology Workflow

TL;DR

This paper introduces a learning-based method that predicts 3D organ locations from a single depth image to automate patient positioning in radiology, enhancing workflow efficiency and reducing manual adjustments.

Contribution

The study presents a novel deep learning framework trained on synthetic depth images from MRI data for accurate 3D organ localization from surface depth images.

Findings

Achieved a mean dice similarity coefficient of 0.44 across 41 structures.

Predicted organ bounding boxes with an average offset of 10.99 mm.

Qualitative results show good generalization to real-world depth images.

Abstract

Automated patient positioning can improve radiology workflow efficiency by reducing the time required for manual table adjustments and scout-based scan planning. We propose a learning-based framework that predicts 3D organ locations and shapes for 41 anatomical structures, including both bones and soft tissues, directly from a single 2D depth image of the body surface. Leveraging $10,020$ whole-body MRI scans from the German National Cohort (NAKO) dataset, we synthetically generate depth images paired with anatomical segmentations to train a convolutional neural network for volumetric organ prediction. Our method achieves a mean dice similarity coefficient of $0.44\pm0.2$ and and a symmetric average surface distance of $7.69\pm5.68$ mm across all structures. Furthermore, the model derives organ bounding boxes with a mean absolute detection offset of $10.99\pm5.54$ mm. Qualitative results on real-world depth images confirm the ability of the model to generalize to practical clinical settings. These findings suggest that depth-only organ localization can support automated patient positioning reducing setup time, minimizing operator variability, and improving patient comfort.