ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image Registration

arXiv:2403.16526v1 Announce Type: new
Abstract: Deformable image registration plays a crucial role in medical imaging, aiding in disease diagnosis and image-guided interventions. Traditional iterative methods are slow, while deep learning (DL) accelerates solutions but faces usability and precision challenges. This study introduces a pyramid network with the enhanced motion decomposition Transformer (ModeTv2) operator, showcasing superior pairwise optimization (PO) akin to traditional methods. We re-implement ModeT operator with CUDA extensions to enhance its computational efficiency. We further propose RegHead module which …

abstract arxiv challenges cs.cv deep learning diagnosis disease disease diagnosis gpu image imaging iterative medical medical imaging network optimization precision pyramid registration role solutions study transformer type usability