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

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

Published on: November 30, 2022

Image registration: Maximum likelihood, minimum entropy and deep learning.

Alireza Sedghi¹, Lauren J O'Donnell², Tina Kapur²

¹Medical Informatics Laboratory, Queen's University, Kingston, Canada.

Medical Image Analysis

|January 3, 2021

Summary

This summary is machine-generated.

This study introduces maximum profile likelihood for image registration, improving joint entropy minimization. The congealing method for groupwise registration is derived, offering a robust alternative to standard mutual information approaches.

Keywords:

Deep learning Image registration Information theory

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Area of Science:

Medical Imaging
Computer Vision
Computational Anatomy

Background:

Image registration is crucial for analyzing medical data.
Existing methods like mutual information have limitations.
Groupwise registration requires robust theoretical frameworks.

Purpose of the Study:

To develop a novel theoretical framework for image registration using maximum profile likelihood.
To derive and optimize methods for pairwise and groupwise registration.
To introduce a deep learning approach for enhanced registration accuracy.

Main Methods:

Maximum profile likelihood framework for pairwise and groupwise registration.
Asymptotic analysis to demonstrate entropy minimization.
Derivation of the congealing method via profile likelihood optimization.
Feature-based registration and deep metric registration using discriminative classifiers.

Main Results:

Maximum profile likelihood registration minimizes an upper bound on joint entropy.
The congealing method is derived for groupwise registration.
A deep metric registration approach is proposed, outperforming standard methods on challenging datasets.
The framework accommodates feature-based and deep learning registration strategies.

Conclusions:

Maximum profile likelihood provides a powerful theoretical foundation for image registration.
The derived methods offer improved accuracy and robustness, especially for groupwise tasks.
Deep metric registration enhances performance and reduces reliance on well-registered training data.