Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks
This work addresses the need for fast and accurate diffeomorphic registration in medical image analysis, offering an incremental improvement over existing unsupervised learning-based techniques.
The paper tackled the problem of diffeomorphic image registration in medical imaging by proposing a patch-based fully convolutional network framework, achieving superior registration accuracy and topology preservation compared to state-of-the-art methods on three T1w MRI datasets.
Diffeomorphic image registration is a fundamental step in medical image analysis, owing to its capability to ensure the invertibility of transformations and preservation of topology. Currently, unsupervised learning-based registration techniques primarily extract features at the image level, potentially limiting their efficacy. This paper proposes a novel unsupervised learning-based fully convolutional network (FCN) framework for fast diffeomorphic image registration, emphasizing feature acquisition at the image patch level. Furthermore, a novel differential operator is introduced and integrated into the FCN architecture for parameter learning. Experiments are conducted on three distinct T1-weighted magnetic resonance imaging (T1w MRI) datasets. Comparative analyses with three state-of-the-art diffeomorphic image registration approaches including a typical conventional registration algorithm and two representative unsupervised learning-based methods, reveal that the proposed method exhibits superior performance in both registration accuracy and topology preservation.