arXiv Open Access 2023

Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration

Junyu Chen Yihao Liu Yufan He Yong Du

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Abstrak

In the past, optimization-based registration models have used spatially-varying regularization to account for deformation variations in different image regions. However, deep learning-based registration models have mostly relied on spatially-invariant regularization. Here, we introduce an end-to-end framework that uses neural networks to learn a spatially-varying deformation regularizer directly from data. The hyperparameter of the proposed regularizer is conditioned into the network, enabling easy tuning of the regularization strength. The proposed method is built upon a Transformer-based model, but it can be readily adapted to any network architecture. We thoroughly evaluated the proposed approach using publicly available datasets and observed a significant performance improvement while maintaining smooth deformation. The source code of this work will be made available after publication.

Topik & Kata Kunci

eess.IV cs.CV

Penulis (4)

Junyu Chen

Yihao Liu

Yufan He

Yong Du

Format Sitasi

APA MLA BibTeX

Chen, J., Liu, Y., He, Y., Du, Y. (2023). Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration. https://arxiv.org/abs/2303.06168

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓