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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

A Physics-Informed Deep Learning Deformable Medical Image Registration Method Based on Neural ODEs.

Amirhossein Amiri-Hezaveh¹, Shelly Tan¹, Qing Deng¹

¹School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 USA.

International Journal of Computer Vision

|September 12, 2025

Summary

This summary is machine-generated.

A new unsupervised machine learning method aligns medical images using solid mechanics principles. This approach accurately models large deformations and biological growth, showing promise in brain imaging and tissue regeneration studies.

Keywords:

Brain atrophy Brain development Brain registration Deformable image registration Growth and remodeling Medical image analysis Neural ordinary differential equations Physics-informed neural networks Zebrafish biophysics

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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

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Published on: November 23, 2019

Area of Science:

Biophysics
Solid Mechanics
Machine Learning

Background:

Accurate medical image registration is crucial for understanding biological processes and disease progression.
Existing methods struggle with large deformations and complex biological growth dynamics.

Purpose of the Study:

Introduce an unsupervised machine learning method for medical image registration.
Incorporate principles of large deformation elasticity, growth, and remodeling.
Validate the method on diverse biological and medical imaging datasets.

Main Methods:

Unsupervised machine learning approach based on minimum potential energy.
Two-step process: predictor step for geometric registration and corrector step for physics enforcement.
Utilizes dissimilarity measures, regularization terms, and potential energy minimization.

Main Results:

Successfully registered medical images with large, non-uniform deformations.
Demonstrated competitive performance against existing methods on brain data.
Applied to zebrafish fin regrowth, cerebral atrophy assessment, and fetal brain development.

Conclusions:

The proposed framework achieves high-quality medical image registration.
Effectively solves large deformation elasticity balance equations and growth/remodeling dynamics.
Offers a versatile tool for analyzing complex biological changes from imaging data.