Deformable Medical Image Registration with Effective Anatomical Structure Representation and Divide-and-Conquer Network

TL;DR

This paper introduces EASR-DCN, a novel ROI-based deformable medical image registration method that independently aligns regions of interest without labels, significantly improving accuracy over existing approaches.

Contribution

The paper proposes a new ROI representation using Gaussian mixture models and a divide-and-conquer network for independent alignment, advancing unsupervised medical image registration.

Findings

Achieved over 10% improvement in Dice score for brain MRI

Enhanced registration accuracy on cardiac and hippocampus MRI datasets

Demonstrated superior deformation reduction compared to VoxelMorph

Abstract

Effective representation of Regions of Interest (ROI) and independent alignment of these ROIs can significantly enhance the performance of deformable medical image registration (DMIR). However, current learning-based DMIR methods have limitations. Unsupervised techniques disregard ROI representation and proceed directly with aligning pairs of images, while weakly-supervised methods heavily depend on label constraints to facilitate registration. To address these issues, we introduce a novel ROI-based registration approach named EASR-DCN. Our method represents medical images through effective ROIs and achieves independent alignment of these ROIs without requiring labels. Specifically, we first used a Gaussian mixture model for intensity analysis to represent images using multiple effective ROIs with distinct intensities. Furthermore, we propose a novel Divide-and-Conquer Network (DCN) to process these ROIs through separate channels to learn feature alignments for each ROI. The resultant correspondences are seamlessly integrated to generate a comprehensive displacement vector field. Extensive experiments were performed on three MRI and one CT datasets to showcase the superior accuracy and deformation reduction efficacy of our EASR-DCN. Compared to VoxelMorph, our EASR-DCN achieved improvements of 10.31\% in the Dice score for brain MRI, 13.01\% for cardiac MRI, and 5.75\% for hippocampus MRI, highlighting its promising potential for clinical applications. The code for this work will be released upon acceptance of the paper.