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To develop a deep learning-based segmentation model for a new image dataset (e. g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches.
We present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that uses insights from classical registration methods and makes use of recent developments in convolutional neural networks (CNNs).
In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images.
#2 best model for Diffeomorphic Medical Image Registration on CUMC12
We define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest.
This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling.
SOTA for BIRL on CIMA-10k
Nonlinear image registration continues to be a fundamentally important tool in medical image analysis.
With the "Autograd Image Registration Laboratory" (AirLab), we introduce an open laboratory for image registration tasks, where the analytic gradients of the objective function are computed automatically and the device where the computations are performed, on a CPU or a GPU, is transparent.
We present recursive cascaded networks, a general architecture that enables learning deep cascades, for deformable image registration.
3D medical image registration is of great clinical importance.
The paper adapts the large deformation diffeomorphic metric mapping framework for image registration to the indirect setting where a template is registered against a target that is given through indirect noisy observations.