BodyGPS: Anatomical Positioning System

Halid Ziya Yerebakan, Kritika Iyer, Xueqi Guo, Yoshihisa Shinagawa, Gerardo Hermosillo Valadez
5/12/2025
cs.CVcs.LG

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

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

84% match2026

Anatomical Positional Embeddings

82% match1 repo52024

LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images

82% match2024

Internal Organ Localization Using Depth Images

82% match1 repo2025

Teaching AI the Anatomy Behind the Scan: Addressing Anatomical Flaws in Medical Image Segmentation with Learnable Prior

80% match2024

MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy Statistical Shape Modeling Directly From Images

80% match2024

Cite this paper

@article{yerebakan2025bodygps,
  title  = {BodyGPS: Anatomical Positioning System},
  author = {Halid Ziya Yerebakan and Kritika Iyer and Xueqi Guo and Yoshihisa Shinagawa and Gerardo Hermosillo Valadez},
  year   = {2025},
  eprint = {2505.07744},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2505.07744v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2505.07744.svg)](https://codeofpaper.com/paper/2505.07744)

Discussion