4 research outputs found
Manifold-Aware CycleGAN for High-Resolution Structural-to-DTI Synthesis
Unpaired image-to-image translation has been applied successfully to natural
images but has received very little attention for manifold-valued data such as
in diffusion tensor imaging (DTI). The non-Euclidean nature of DTI prevents
current generative adversarial networks (GANs) from generating plausible images
and has mainly limited their application to diffusion MRI scalar maps, such as
fractional anisotropy (FA) or mean diffusivity (MD). Even if these scalar maps
are clinically useful, they mostly ignore fiber orientations and therefore have
limited applications for analyzing brain fibers. Here, we propose a
manifold-aware CycleGAN that learns the generation of high-resolution DTI from
unpaired T1w images. We formulate the objective as a Wasserstein distance
minimization problem of data distributions on a Riemannian manifold of
symmetric positive definite 3x3 matrices SPD(3), using adversarial and
cycle-consistency losses. To ensure that the generated diffusion tensors lie on
the SPD(3) manifold, we exploit the theoretical properties of the exponential
and logarithm maps of the Log-Euclidean metric. We demonstrate that, unlike
standard GANs, our method is able to generate realistic high-resolution DTI
that can be used to compute diffusion-based metrics and potentially run fiber
tractography algorithms. To evaluate our model's performance, we compute the
cosine similarity between the generated tensors principal orientation and their
ground-truth orientation, the mean squared error (MSE) of their derived FA
values and the Log-Euclidean distance between the tensors. We demonstrate that
our method produces 2.5 times better FA MSE than a standard CycleGAN and up to
30% better cosine similarity than a manifold-aware Wasserstein GAN while
synthesizing sharp high-resolution DTI.Comment: Accepted at MICCAI 2020 International Workshop on Computational
Diffusion MR
Adversarial normalization for multi domain image segmentation
Image normalization is a critical step in medical imaging. This step is often
done on a per-dataset basis, preventing current segmentation algorithms from
the full potential of exploiting jointly normalized information across multiple
datasets. To solve this problem, we propose an adversarial normalization
approach for image segmentation which learns common normalizing functions
across multiple datasets while retaining image realism. The adversarial
training provides an optimal normalizer that improves both the segmentation
accuracy and the discrimination of unrealistic normalizing functions. Our
contribution therefore leverages common imaging information from multiple
domains. The optimality of our common normalizer is evaluated by combining
brain images from both infants and adults. Results on the challenging iSEG and
MRBrainS datasets reveal the potential of our adversarial normalization
approach for segmentation, with Dice improvements of up to 59.6% over the
baseline.Comment: Submitted to ISBI 202
Realistic Image Normalization for Multi-Domain Segmentation
Image normalization is a building block in medical image analysis.
Conventional approaches are customarily utilized on a per-dataset basis. This
strategy, however, prevents the current normalization algorithms from fully
exploiting the complex joint information available across multiple datasets.
Consequently, ignoring such joint information has a direct impact on the
performance of segmentation algorithms. This paper proposes to revisit the
conventional image normalization approach by instead learning a common
normalizing function across multiple datasets. Jointly normalizing multiple
datasets is shown to yield consistent normalized images as well as an improved
image segmentation. To do so, a fully automated adversarial and task-driven
normalization approach is employed as it facilitates the training of realistic
and interpretable images while keeping performance on-par with the
state-of-the-art. The adversarial training of our network aims at finding the
optimal transfer function to improve both the segmentation accuracy and the
generation of realistic images. We evaluated the performance of our normalizer
on both infant and adult brains images from the iSEG, MRBrainS and ABIDE
datasets. Results reveal the potential of our normalization approach for
segmentation, with Dice improvements of up to 57.5% over our baseline. Our
method can also enhance data availability by increasing the number of samples
available when learning from multiple imaging domains