Enhancing medical image registration via appearance adjustment networks

  title={Enhancing medical image registration via appearance adjustment networks},
  author={Mingyuan Meng and Lei Bi and Michael J. Fulham and David Dagan Feng and Jinman Kim},

Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learning

Extensive experiments on six public datasets of 3D brain Magnetic Resonance Imaging (MRI) show that the proposed NICE-Net can outperform state-of-the-art iterative deep registration methods while only requiring similar runtime to non-iterative methods.



Advanced Normalization Tools (ANTs)

Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks

A deep Laplacian Pyramid Image Registration Network is proposed, which can solve the image registration optimization problem in a coarse-to-fine fashion within the space of diffeomorphic maps.

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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

A lower entry barrier for training and validation of 3D registration is established and a complementary set of metrics, including robustness, accuracy, plausibility and speed enables unique insight into the current-state-of-the-art of medical image registration.

Metamorphic image registration using a semi-Lagrangian scheme

In this paper, we propose an implementation of both Large Deformation Diffeomorphic Metric Mapping (LDDMM) and Metamorphosis image registration using a semi-Lagrangian scheme for geodesic shooting.

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The xishacorene natural products are structurally unique apolar diterpenoids that feature a bicyclo[3.3.1] framework. These secondary metabolites likely arise from the well-studied, structurally

Learning Joint Shape and Appearance Representations with Metamorphic Auto-Encoders

This work introduces the “metamorphic” auto-encoding architecture, a class of neural networks interpreted as a Bayesian generative and hierarchical model, allowing the joint estimation of the network parameters, a representative prototype of the training images, as well as the relative importance between the geometrical and texture contents.