Automated Patient Positioning with Learned 3D Hand Gestures

TL;DR

This paper introduces an automated patient positioning system that uses hand gesture recognition via RGB-Depth cameras to improve accuracy and efficiency during MRI procedures, reducing manual effort.

Contribution

It presents a novel multi-stage pipeline for recognizing technician gestures and translating them into precise device movements, validated in clinical MRI settings.

Findings

Achieves accurate patient positioning with minimal technician intervention

Demonstrates effective hand gesture recognition on HaGRID dataset

Validates system performance during actual MRI scans

Abstract

Positioning patients for scanning and interventional procedures is a critical task that requires high precision and accuracy. The conventional workflow involves manually adjusting the patient support to align the center of the target body part with the laser projector or other guiding devices. This process is not only time-consuming but also prone to inaccuracies. In this work, we propose an automated patient positioning system that utilizes a camera to detect specific hand gestures from technicians, allowing users to indicate the target patient region to the system and initiate automated positioning. Our approach relies on a novel multi-stage pipeline to recognize and interpret the technicians' gestures, translating them into precise motions of medical devices. We evaluate our proposed pipeline during actual MRI scanning procedures, using RGB-Depth cameras to capture the process. Results show that our system achieves accurate and precise patient positioning with minimal technician intervention. Furthermore, we validate our method on HaGRID, a large-scale hand gesture dataset, demonstrating its effectiveness in hand detection and gesture recognition.