What is the state of the art in multimodal 3D rigid registration of medical images with Deep Learning?

The state-of-the-art in registration of multimodal 3D medical images using deep learning methods is a rapidly evolving field. One of the most popular approaches is to use convolutional neural networks (CNNs) to learn a mapping function between pairs of images to be registered.

There are several different network architectures and training strategies that have been proposed for this task, but some common ones include:

Siamese Networks: These networks take two input images (fixed and moving), pass them through identical CNNs, and then compute a similarity metric between the feature vectors outputs by the CNNs. The similarity metric can be used as a measure of the alignment between the two images, which can then be optimized during training.

Spatial Transformer Networks (STNs): STNs are deep neural networks that can learn to perform spatial transformations on an input image, such as rotation, scaling, or translation, through a learned deformation field.

Generative Adversarial Networks (GANs): GANs consist of a generator network that produces transformed images from the input data, and a discriminator network that distinguishes between real and generated images. During training, the generator learns to produce realistic transformed images, while the discriminator learns to differentiate between real and generated images.

Variational Autoencoders (VAEs): VAEs are unsupervised deep learning models that can learn low-dimensional representations of high-dimensional data. They consist of an encoder network that maps the input images to a lower-dimensional space, and a decoder network that reconstructs the original images from this representation.

In terms of the data you have provided, a potential approach would be to use a 3D CNN architecture to learn a mapping function between the fixed and moving images. The output of the network could be a deformation field that captures the transformation required to align the two images. This deformation field could then be used to warp the moving image to align with the fixed image.

It's worth noting that multimodal registration is generally more challenging than monomodal registration, as there may be significant differences in image contrast and intensity between the modalities. One potential strategy to address this challenge is to use a multi-branch CNN architecture that can process each modality separately before fusing the features at a later stage.

Overall, multimodal 3D rigid registration using deep learning methods is an active area of research, and new approaches are being developed and optimized all the time.

Matlab Code:

% Load fixed and moving images

fixed = load_nii('fixed.nii');

moving = load_nii('moving.nii');

% Define network architecture

layers = [

imageInputLayer([size(fixed.img,1), size(fixed.img,2), size(fixed.img,3), 1])

convolution3dLayer(3, 16, 'Padding', 'same')

batchNormalizationLayer

reluLayer

maxPooling3dLayer(2, 'Stride', 2)

convolution3dLayer(3, 32, 'Padding', 'same')

batchNormalizationLayer

reluLayer

maxPooling3dLayer(2, 'Stride', 2)

convolution3dLayer(3, 64, 'Padding', 'same')

batchNormalizationLayer

reluLayer

fullyConnectedLayer(3)

regressionLayer];

% Define loss function (mean squared error)

lossFcn = @(x, t) mean((x - t).^2);

% Define training options

options = trainingOptions('adam', ...

'MaxEpochs', 10, ...

'MiniBatchSize', 32, ...

'InitialLearnRate', 0.001, ...

'Shuffle', 'every-epoch', ...

'ExecutionEnvironment', 'gpu', ...

'Plots', 'training-progress');

% Preprocess images (normalize intensities)

fixed.img = normalize(fixed.img);

moving.img = normalize(moving.img);

% Create training data

XTrain = reshape(moving.img, [size(moving.img), 1, 1]);

YTrain = zeros(size(XTrain,1), size(XTrain,2), size(XTrain,3), 3);

YTrain(:,:,:,1) = linspace(-1, 1, size(XTrain,1));

YTrain(:,:,:,2) = linspace(-1, 1, size(XTrain,2));

YTrain(:,:,:,3) = linspace(-1, 1, size(XTrain,3));

% Train network

net = trainNetwork(XTrain, YTrain, layers, options);

% Use network to predict transformation on test image

test = load_nii('test.nii');

test.img = normalize(test.img);

XTest = reshape(test.img, [size(test.img), 1, 1]);

YPred = predict(net, XTest);

deformationField = reshape(YPred, [size(YPred,1), size(YPred,2), size(YPred,3), 3]);

% Apply deformation field to moving image

warped = imwarp(moving.img, deformationField);

% Save results

fixed.hdr.hist.qform_code = 1;

fixed.hdr.hist.sform_code = 1;

fixed.hdr.hist.quatern_d = 0;

fixed.hdr.hist.quatern_c = 0;

fixed.hdr.hist.quatern_b = 0;

fixed.hdr.hist.quatern_a = 1;

fixed.hdr.hist.srow_x = [1 0 0 -floor(size(fixed.img,1)/2)];

fixed.hdr.hist.srow_y = [0 1 0 -floor(size(fixed.img,2)/2)];

fixed.hdr.hist.srow_z = [0 0 1 -floor(size(fixed.img,3)/2)];

fixed.hdr.dime.dim(5) = 1;

warpedNifti = make_nii(warped, [], [], [], 'Warped Moving Image');

save_nii(warpedNifti, 'warped.nii');

Tajinder Kumar Saini

Convolutional Neural Networks (CNNs): CNNs have been adapted for rigid registration tasks by treating registration as a regression problem, where the network learns to predict the transformation parameters between the two input images directly. The CNNs take pairs of images as input and output the transformation parameters, enabling end-to-end registration.

Spatial Transform Networks (STN): STNs are a type of neural network module that allows the network to learn spatial transformations. STNs can be incorporated into a registration pipeline to learn and apply the necessary transformations between the images.

Image Synthesis: Generative models like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) have been employed to synthesize one modality from another, enabling the transformation of the multimodal images into a common space for registration.

Self-Supervised Learning: Self-supervised learning approaches have been explored, where the network learns registration by designing a pretext task that does not require ground truth correspondences between the images. These methods leverage the inherent multimodal information within the images.

Attention Mechanisms: Attention mechanisms have been integrated into registration networks to focus on informative image regions and improve the alignment process.

Large-Scale Datasets and Transfer Learning: Some researchers have used large-scale datasets or pre-trained models from unrelated tasks (e.g., ImageNet) for transfer learning, boosting the performance of registration networks on smaller medical image datasets.

Metric Learning: Metric learning techniques have been employed to learn distance metrics or similarity functions between images, allowing for more robust and discriminative registration.

How can I measure MMP-1 protein activity or protein level from liver tissue?

Binary Logistic Generalized Linear Mixed model in SPSS. Is it doable?

Looking for Materials with high thermal expansion coefficient and high temperature resistance?

Guidelines for survey (question type) analysis?

What are the values needed for gabor filter's parameter for cifar10 data?

Can we block unwanted images during the x-ray prediction?

No title

What techniques can I use to determine the miscibility of polymer blends?

Sample dilution for quantitative analysis using ELISA?

The procedure compute?

Can we classify HV and LV lines based on line length?

Anyone plz suggest relevant topic for research in image processing+Deep learning.?

Can we block unwanted images during the x-ray prediction?

I need to image mesoporous silica nanoparticles that are in ~100 nm size and do high resolution TEM to look at mesopores. what grids should I use?

What analysis to choose with a large neste dataset and clearly skewed or kurtotic distributions?

Do I need to strip the PVDF membrane before staining it with colloidal gold?

What do you know about biotechnology art?

Do you know any MR image dataset?

Can NFL theorem be valid in infinite search space in ML?

How to find the mode of classified ocean colour images?