用深度图像预测体内器官位置，实现MRI自动定位。
Internal Organ Localization Using Depth Images
- 基于深度图和深度学习，从体表推断内部器官位置。
- 在多类器官（骨骼与软组织）上实现高精度定位。
- 适合需要提升MRI流程效率的医疗场景。
自动化患者定位是优化MRI工作流程、提升患者通量的关键步骤。基于RGB-D相机的系统通过利用深度信息估算内部器官位置，展现出良好的应用前景。本文研究了一种基于学习的框架，仅凭体表深度图像即可推断内部器官的大致位置。该方法依托大规模MRI扫描数据集，训练深度学习模型，实现了从深度图像准确预测器官位置与形态的能力。实验验证了该方法在多个内部器官（包括骨骼与软组织）定位上的有效性。结果表明，集成于MRI工作流程的RGB-D相机系统有望通过实现精准、自动化的患者定位，优化扫描流程并改善患者体验。
原文摘要 · Abstract (English)
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.
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