BodyGPS: Anatomical Positioning System

TL;DR

BodyGPS is a versatile foundational model for human anatomy parsing in medical images, capable of various tasks across modalities with high efficiency and minimal response time, supporting both supervised and unsupervised training.

Contribution

It introduces a neural network-based anatomical positioning system that works across modalities and tasks, with fast response times and flexible training options.

Findings

Effective in CT and MRI modalities

Response times under 1 ms

Supports multiple tasks with or without user interaction

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.