A Symmetric Dynamic Learning Framework for Diffeomorphic Medical Image Registration

TL;DR

This paper presents DCCNN-LSTM-Reg, a novel symmetric, dynamic deep learning framework for diffeomorphic medical image registration that ensures topology preservation and improves registration accuracy.

Contribution

It introduces a new learning framework combining deep networks with diffeomorphic principles to achieve symmetric, continuous, and dynamic registration across multiple scales.

Findings

Outperforms existing methods in quantitative metrics

Achieves superior qualitative registration results

Effective across three 3D registration tasks

Abstract

Diffeomorphic image registration is crucial for various medical imaging applications because it can preserve the topology of the transformation. This study introduces DCCNN-LSTM-Reg, a learning framework that evolves dynamically and learns a symmetrical registration path by satisfying a specified control increment system. This framework aims to obtain symmetric diffeomorphic deformations between moving and fixed images. To achieve this, we combine deep learning networks with diffeomorphic mathematical mechanisms to create a continuous and dynamic registration architecture, which consists of multiple Symmetric Registration (SR) modules cascaded on five different scales. Specifically, our method first uses two U-nets with shared parameters to extract multiscale feature pyramids from the images. We then develop an SR-module comprising a sequential CNN-LSTM architecture to progressively correct the forward and reverse multiscale deformation fields using control increment learning and the homotopy continuation technique. Through extensive experiments on three 3D registration tasks, we demonstrate that our method outperforms existing approaches in both quantitative and qualitative evaluations.