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Main Authors: Yerebakan, Halid Ziya, Iyer, Kritika, Guo, Xueqi, Shinagawa, Yoshihisa, Valadez, Gerardo Hermosillo
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2505.07744
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author Yerebakan, Halid Ziya
Iyer, Kritika
Guo, Xueqi
Shinagawa, Yoshihisa
Valadez, Gerardo Hermosillo
author_facet Yerebakan, Halid Ziya
Iyer, Kritika
Guo, Xueqi
Shinagawa, Yoshihisa
Valadez, Gerardo Hermosillo
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2505_07744
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle BodyGPS: Anatomical Positioning System
Yerebakan, Halid Ziya
Iyer, Kritika
Guo, Xueqi
Shinagawa, Yoshihisa
Valadez, Gerardo Hermosillo
Computer Vision and Pattern Recognition
Machine Learning
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.
title BodyGPS: Anatomical Positioning System
topic Computer Vision and Pattern Recognition
Machine Learning
url https://arxiv.org/abs/2505.07744