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

2026-01-26

AI Summary

This study addresses the challenge of accurately estimating the three-dimensional positions and shapes of internal organs from a single body surface depth image to enable automated patient positioning in radiological scanning. To this end, the authors propose an end-to-end learning framework trained on large-scale whole-body MRI data, which is used to synthesize paired depth maps and anatomical segmentation labels. The model directly predicts the 3D morphology of multiple skeletal and soft-tissue structures without requiring explicit surface reconstruction. This work presents the first approach to jointly model depth perception and internal anatomical structure prediction, demonstrating effective localization of multiple key organs. The results offer a practical pathway toward integrating depth sensors into radiology workflows for efficient and automated patient positioning.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• depth image
• internal organ localization
• automated patient positioning
• anatomical structure
• medical imaging

Innovation

• depth imaging
• anatomical localization
• learning-based framework
• automated patient positioning
• convolutional neural network