In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration. This work is built on top of recent algorithm self-supervised anatomical embedding (SAM), which capable computing dense anatomical/semantic correspondences between two images at the pixel level. Our named SAM-enhanced registration (SAME), breaks down into three steps: affine transformation, coa...

Groupwise image registration describes the problem of simultaneously aligning a series more than two images through individual spatial deformations and it is common task in processing medical sequences. Variational methods with data fidelity terms based on robust PCA (RPCA) have proven successful accounting for structural changes intensity stemming, e.g., from uptake contrast agent functional i...

Deformable medical image registration plays a vital role in applications, such as placing different temporal images at the same time point or modality into coordinate system. Various strategies have been developed to satisfy increasing needs of deformable registration. One popular method is estimating displacement field by computing optical flow between two images. The motion (flow field) compu...