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Found 4 papers, 1 papers with code
Diffeomorphic Transformer-based Abdomen MRI-CT Deformable Image Registration
By using topology-preserved deformation features extracted from the probabilistic diffeomorphic registration model, abdominal motion can be accurately obtained and utilized for DVF estimation.
Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model
The proposed model consists of two processes: a forward process which adds Gaussian noise to real CT scans, and a reverse process in which a shifted-window transformer V-net (Swin-Vnet) denoises the noisy CT scans conditioned on the MRI from the same patient to produce noise-free CT scans.
Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory
We further tested the proposed model on 69 landmarks from the testing dataset that has a similar image pattern to the training pattern, resulting in a mean tracking error of 0. 94+/-0. 83 mm.
Deformable Image Registration using Unsupervised Deep Learning for CBCT-guided Abdominal Radiotherapy
The STN consists of a global generative adversarial network (GlobalGAN) and a local GAN (LocalGAN) to predict the coarse- and fine-scale motions, respectively.
