The multimodal brain tumor image segmentation benchmark (BRATS)
In this paper we report the set-up and results of the Multimodal Brain Tumor Image
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …
Image synthesis in multi-contrast MRI with conditional generative adversarial networks
Acquiring images of the same anatomy with multiple different contrasts increases the
diversity of diagnostic information available in an MR exam. Yet, the scan time limitations …
diversity of diagnostic information available in an MR exam. Yet, the scan time limitations …
Synseg-net: Synthetic segmentation without target modality ground truth
A key limitation of deep convolutional neural network (DCNN)-based image segmentation
methods is the lack of generalizability. Manually traced training images are typically required …
methods is the lack of generalizability. Manually traced training images are typically required …
Prior-guided image reconstruction for accelerated multi-contrast MRI via generative adversarial networks
Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of information available
for diagnosis. Yet, excessive scan times associated with additional contrasts may be a …
for diagnosis. Yet, excessive scan times associated with additional contrasts may be a …
Estimating CT image from MRI data using structured random forest and auto-context model
Computed tomography (CT) imaging is an essential tool in various clinical diagnoses and
radiotherapy treatment planning. Since CT image intensities are directly related to positron …
radiotherapy treatment planning. Since CT image intensities are directly related to positron …
mustGAN: multi-stream generative adversarial networks for MR image synthesis
Multi-contrast MRI protocols increase the level of morphological information available for
diagnosis. Yet, the number and quality of contrasts are limited in practice by various factors …
diagnosis. Yet, the number and quality of contrasts are limited in practice by various factors …
Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image
Recently, more and more attention is drawn to the field of medical image synthesis across
modalities. Among them, the synthesis of computed tomography (CT) image from T1 …
modalities. Among them, the synthesis of computed tomography (CT) image from T1 …
[HTML][HTML] DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis
Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-
domain medical image synthesis tasks particularly due to its ability to deal with unpaired …
domain medical image synthesis tasks particularly due to its ability to deal with unpaired …
Cola-diff: Conditional latent diffusion model for multi-modal mri synthesis
MRI synthesis promises to mitigate the challenge of missing MRI modality in clinical practice.
Diffusion model has emerged as an effective technique for image synthesis by modelling …
Diffusion model has emerged as an effective technique for image synthesis by modelling …
Random forest regression for magnetic resonance image synthesis
By choosing different pulse sequences and their parameters, magnetic resonance imaging
(MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield …
(MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield …