Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks

• URL: http://arxiv.org/abs/2404.04244v2
• Date: Sat, 4 May 2024 01:06:54 GMT
• Title: Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks
• Authors: Jiong Wu, Shuang Zhou, Li Lin, Xin Wang, Wenxue Tan,
• Abstract summary: This paper proposes a novel unsupervised learning-based fully convolutional network (FCN) framework for fast diffeomorphic image registration. Experiments are conducted on three distinct T1-weighted magnetic resonance imaging (T1w MRI) datasets.
• Score: 5.479932919974457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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