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Found 16 papers, 7 papers with code
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction
We demonstrate that our proposed method produces high-quality reconstructions as well as uncertainty quantification that is substantially better calibrated than several strong baselines.
Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling
We evaluate our approach on 92 in-house 2D+t cardiac MR subjects and compare it to MR reconstruction methods with image-domain regularizers.
Reconstruction-driven motion estimation for motion-compensated MR CINE imaging
Contrary to state-of-the-art (SOTA) MCMR methods which break the original problem into two sub-optimization problems, i. e. motion estimation and reconstruction, we formulate this problem as a single entity with one single optimization.
Learning-based and unrolled motion-compensated reconstruction for cardiac MR CINE imaging
Motion-compensated MR reconstruction (MCMR) is a powerful concept with considerable potential, consisting of two coupled sub-problems: Motion estimation, assuming a known image, and image reconstruction, assuming known motion.
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging
We consider inverse problems with both linear and non-linear forward models for computational MRI, and review the classical approaches for solving these.
LAPNet: Non-rigid Registration derived in k-space for Magnetic Resonance Imaging
Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts.
Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction
The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatio-temporal redundancies in complementary domains.
Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation
Thus, a whole-body assessment of the BA does not reflect the deviations of aging behavior between organs.
Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies
Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions.
Organ-based Chronological Age Estimation based on 3D MRI Scans
Individuals age differently depending on a multitude of different factors such as lifestyle, medical history and genetics.
Unsupervised Adversarial Correction of Rigid MR Motion Artifacts
Motion is one of the main sources for artifacts in magnetic resonance (MR) images.
Unsupervised Medical Image Translation Using Cycle-MedGAN
The proposed framework utilizes new non-adversarial cycle losses which direct the framework to minimize the textural and perceptual discrepancies in the translated images.
Retrospective correction of Rigid and Non-Rigid MR motion artifacts using GANs
Motion artifacts are a primary source of magnetic resonance (MR) image quality deterioration with strong repercussions on diagnostic performance.
A Machine-learning framework for automatic reference-free quality assessment in MRI
Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest.
MedGAN: Medical Image Translation using GANs
Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications.
