Deep Convolutional Neural Network for Non-rigid Image Registration

TL;DR

This paper demonstrates that a deep convolutional neural network can perform non-rigid image registration more efficiently than traditional methods, reducing computational time significantly.

Contribution

It introduces a CNN-based approach for non-rigid image registration, offering a faster alternative to existing computationally intensive methods.

Findings

CNN achieves comparable registration accuracy to traditional methods

Significantly reduces computational time for non-rigid registration

Potential for real-time applications in medical imaging

Abstract

Images taken at different times or positions undergo transformations such as rotation, scaling, skewing, and more. The process of aligning different images which have undergone transformations can be done via registration. Registration is desirable when analyzing time-series data for tracking, averaging, or differential diagnoses of diseases. Efficient registration methods exist for rigid (including linear or affine) transformations; however, for non-rigid (also known as non-affine) transformations, current methods are computationally expensive and time-consuming. In this report, I will explore the ability of a deep neural network (DNN) and, more specifically, a deep convolutional neural network (CNN) to efficiently perform non-rigid image registration. The experimental results show that a CNN can be used for efficient non-rigid image registration and in significantly less computational time than a conventional Diffeomorphic Demons or Pyramiding approach.