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[bibtex]@InProceedings{Kim_2025_WACV, author = {Kim, Daniel and Al-masni, Mohammed A. and Lee, Jaehun and Kim, Dong-Hyun and Ryu, Kanghyun}, title = {Improving Pelvic MR-CT Image Alignment with Self-Supervised Reference-Augmented Pseudo-CT Generation Framework}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {347-356} }
Improving Pelvic MR-CT Image Alignment with Self-Supervised Reference-Augmented Pseudo-CT Generation Framework
Abstract
RegistFormer our novel reference-augmented image synthesis framework generates aligned pseudo-CT images (with respect to MR) from misaligned MR and CT pairs. RegistFormer addresses the limitations of intensity-based registration methods which often fail due to dissimilar image features and complex deformation fields. Unlike conventional image-to-image (I2I) translation methods our method uses a misaligned CT scan as an auxiliary input to guide the synthesis task through the Deformation-Aware Cross-Attention (DACA) mechanism. DACA integrates the deformation field from a registration method to aggregate spatially matched features from the misaligned CT into MR spatial coordinates. Additionally we propose a novel combination of loss functions for training with datasets of misaligned MR-CT pairs in a self-supervised manner eliminating the need for pre-aligned training data. Experiments were conducted with the synthRAD2023 MR-CT pelvis pair dataset. RegistFormer outperforms past state-of-the-art methods including I2I registration and hybrid (registration + I2I) across metrics evaluating both structure alignment and distribution similarity. Moreover RegistFormer demonstrates superior performance in zero-shot segmentation downstream tasks highlighting its clinical value. Source code: https://github.com/danny4159/RegistFormer
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