BodyGPS: Anatomical Positioning System

2025/05/12 by Halid Ziya Yerebakan, Kritika Iyer, Yerebakan, Halid Ziya +7
Computer Science · Engineering · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #Medical Imaging and Analysis

paper · pdf · doi:10.48550/arxiv.2505.07744

openalex publication_date 2025/05/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We introduce a new type of foundational model for parsing human anatomy in medical images that works for different modalities. It supports supervised or unsupervised training and can perform matching, registration, classification, or segmentation with or without user interaction. We achieve this by training a neural network estimator that maps query locations to atlas coordinates via regression. Efficiency is improved by sparsely sampling the input, enabling response times of less than 1 ms without additional accelerator hardware. We demonstrate the utility of the algorithm in both CT and MRI modalities.

Citations

• MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging
• Deep Learning in Medical Image Registration: Magic or Mirage?
• Real Time Multi Organ Classification on Computed Tomography Images
• FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images
• A Hierarchical Descriptor Framework for On-the-Fly Anatomical Location Matching between Longitudinal Studies
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• UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation
• TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images
• One-Shot Medical Landmark Detection
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• UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
• U-Net: Convolutional Networks for Biomedical Image Segmentation

Related

• Anatomical Positional Embeddings
• Teaching AI the Anatomy Behind the Scan: Addressing Anatomical Flaws in Medical Image Segmentation with Learnable Prior
• Generalised Automatic Anatomy Finder (GAAF): A general framework for 3D location-finding in CT scans
• Depth to Anatomy: Organ Localization from Depth Images for Automated Patient Table Positioning in Radiology Workflow
• SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images