# BodyGPS: Anatomical Positioning System

arXiv: https://arxiv.org/abs/2505.07744 · HTML: https://mlsanity.com/paper/2505.07744/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation](https://mlsanity.com/paper/2212.04497/) (2022)
- [MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging](https://mlsanity.com/paper/2410.06542/) (2024)
- [One-Shot Medical Landmark Detection](https://mlsanity.com/paper/2103.04527/) (2021)
- [Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images](https://mlsanity.com/paper/2007.05295/) (2020)
- [Deep Learning in Medical Image Registration: Magic or Mirage?](https://mlsanity.com/paper/2408.05839/) (2024)
- [vox2vec: A Framework for Self-supervised Contrastive Learning of Voxel-level Representations in Medical Images](https://mlsanity.com/paper/2307.14725/) (2023)
- [Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images](https://mlsanity.com/paper/2012.07043/) (2020)
- [FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images](https://mlsanity.com/paper/2403.09827/) (2024)
- [A Hierarchical Descriptor Framework for On-the-Fly Anatomical Location Matching between Longitudinal Studies](https://mlsanity.com/paper/2308.07337/) (2023)
- [Real Time Multi Organ Classification on Computed Tomography Images](https://mlsanity.com/paper/2404.18731/) (2024)

## Abstract (arXiv)

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.