BodyGPS: Anatomical Positioning System
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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.
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.
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References · 14
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 662
- 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 613
- 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 586
- 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
- 2024MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imagingcited by 46
- 2021One-Shot Medical Landmark Detectioncited by 31
- 2020Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Imagescited by 22
- 2024Deep Learning in Medical Image Registration: Magic or Mirage?cited by 21
- 2023vox2vec: A Framework for Self-supervised Contrastive Learning of Voxel-level Representations in Medical Imagescited by 19
- 2020Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Imagescited by 17
- 2024FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Imagescited by 14
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