![]() Malte Hoffmann, Benjamin Billot, Juan Eugenio Iglesias, Bruce Fischl, Adrian V. ![]() SynthMorph: learning contrast-invariant registration without acquired images. Malte Hoffmann, Andrew Hoopes, Bruce Fischl, Adrian V. SynthMorph, avoiding the need to have data at training (!):Īnatomy-specific acquisition-agnostic affine registration learned from fictitious images. IPMI: Information Processing in Medical Imaging. HyperMorph: Amortized Hyperparameter Learning for Image Registration. MELBA: Machine Learning for Biomedical Imaging. Learning the Effect of Registration Hyperparameters with HyperMorphĪndrew Hoopes, Malte Hoffmann, Bruce Fischl, John Guttag, Adrian V. HyperMorph, avoiding the need to tune registration hyperparameters: If you use VoxelMorph or some part of the code, please cite (see bibtex): By default we integrate using scaling and squaring, which we found efficient. Note that original development of VoxelMorph used xy indexing, whereas we are now emphasizing ij indexing.įor the MICCAI2018 version, we integrate the velocity field using. The spatial transform code, found at, accepts N-dimensional affine and dense transforms, including linear and nearest neighbor interpolation options. However, if you'd like to run the very original MICCAI2018 mode, please use xy indexing and use_miccai_int network option, with MICCAI2018 parameters. With the newest code, this has been "fixed", with different default parameters reflecting the change. In the original MICCAI code, the parameters were applied after the scaling of the velocity field. ![]() MICCAI versionįor our data, we found image_sigma=0.01 and prior_lambda=25 to work best. For the MSE loss function, we found 0.01 to work best. Parameter choices CVPR versionįor the CC loss function, we found a reg parameter of 1 to work best. Just like for the training data, the atlas and test npz files include vol and seg parameters and the labels.npz file contains a list of corresponding anatomical labels to include in the computed dice score. scripts/tf/test.py -model model.h5 -atlas atlas.npz -scans scan01.npz scan02.npz scan03.npz -labels labels.npz Model weights will be saved to a path specified by the -model-dir flag. It's also assumed that the shape of all training image data is consistent, but this, of course, can be handled in a customized generator if desired.įor a given image list file /images/list.txt and output directory /models/output, the following script will train an image-to-image registration network (described in MICCAI 2018 by default) with an unsupervised loss. ![]() Training data can be in the NIfTI, MGZ, or npz (numpy) format, and it's assumed that each npz file in your data list has a vol parameter, which points to the image data to be registered, and an optional seg variable, which points to a corresponding discrete segmentation (for semi-supervised learning). However, it is possible to run many of the example scripts out-of-the-box, assuming that you provide a list of filenames in the training dataset. If you would like to train your own model, you will likely need to customize some of the data-loading code in voxelmorph/generators.py for your own datasets and data formats. See list of pre-trained models available here. ![]()
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