BIDMIR: BI-DIRECTIONAL MEDICAL IMAGE REGISTRATION WITH SYMMETRIC ATTENTION AND CYCLIC CONSISTENCY REGULARIZATION
2022 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (IEEE ISBI 2022)(2022)
摘要
The past years witness the remarkable progress in developing deep learning-based image registration methods, which leverage convolutional neural networks (CNNs) for efficient and end-to-end regression of deformation fields from an image pair. Identified limitations of existing methods include (a) ignorance of intra- and inter-image long-range spatial relevance, leading to failure in finding semantically meaningful correspondences of anatomical structures; and (b) single direction of image registration with no enforcement of topology preservation, resulting in loss of structural information. To address these issues, we propose a novel bi-directional medical image registration method, referred as BIDMIR, integrating symmetric attention with cyclic consistency regularization. The proposed method consists of a learnable volumetric embedding module, a symmetric attention module for feature enhancement, and a bi-directional registration field inference module. The symmetric attention module explicitly models the intra- and inter-image long-range relevance in the embedding, facilitating bi-directional correspondence of semantically meaningful structures. Cyclic consistency regularization is additionally proposed to encourage topology preservation. Results demonstrate the efficacy of our approach.
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关键词
Deep learning, Bi-directional medical image registration, Symmetric attention, Cyclic consistency
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