BodyGPS: Anatomical Positioning System

• URL: http://arxiv.org/abs/2505.07744v1
• Date: Mon, 12 May 2025 16:53:41 GMT
• Title: BodyGPS: Anatomical Positioning System
• Authors: Halid Ziya Yerebakan, Kritika Iyer, Xueqi Guo, Yoshihisa Shinagawa, Gerardo Hermosillo Valadez,
• Abstract summary: We introduce a new type of foundational model for parsing human anatomy in medical images.<n>It supports supervised or unsupervised training and can perform matching, registration, classification, or segmentation with or without user interaction.<n>We demonstrate the utility of the algorithm in both CT and MRI modalities.
• Score: 0.0699049312989311
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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