The registration of multimodal images remains an intricate issue, especially when the multimodal image pair shows non overlapping structures, missing data, noise or outliers. In this paper, we present a deformable model-based technique for the rigid registration of 2 0 and 3D multimodal images. The deformable model embeds a priori knowledge of the spatial correspondence and statistical variabil...

Deformable models, a promising and vigorously researched model-based approach to computer-assisted medical image analysis. The widely recognized potency of deformable models stems from their ability to segment, match, and track images of anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size, and...

Active contour and surface models, also known as deformable models, are powerful image segmentation techniques. Geometric deformable models implemented using level set methods have advantages over parametric models due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models — the ability to automati...

Temporal volume images with 3D+t (4D) information are often used in medical imaging to statistically analyze temporal dynamics or capture disease progression. Although deep-learning-based generative models for natural have been extensively studied, approaches image generation such as 4D cardiac data limited. In this work, we present a novel deep learning model that generates intermediate volume...

We propose a lightweight neural network model, Deformable Volume Network (Devon) for learning optical flow. Devon benefits from a multi-stage framework to iteratively refine its prediction. Each stage is by itself a neural network with an identical architecture. The optical flow between two stages is propagated with a newly proposed module, the deformable cost volume. The deformable cost volume...