73 citations · 305 across the 55 of their papers we have counts for
17 papers · 1 filter
FetalDiffusion: Pose-Controllable 3D Fetal MRI Synthesis with Conditional Diffusion Model
Molin Zhang, Polina Golland, Patricia Ellen Grant +1
The quality of fetal MRI is significantly affected by unpredictable and substantial fetal motion, leading to the introduction of artifacts even when fast acquisition sequences are…
Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series
Zeen Chi, Zhongxiao Cong, Clinton J. Wang +6
We present a method for fast biomedical image atlas construction using neural fields. Atlases are key to biomedical image analysis tasks, yet conventional and deep network estimati…
Domain-agnostic segmentation of thalamic nuclei from joint structural and diffusion MRI
Henry F. J. Tregidgo, Sonja Soskic, Mark D. Olchanyi +9
The human thalamus is a highly connected subcortical grey-matter structure within the brain. It comprises dozens of nuclei with different function and connectivity, which are affec…
Data Consistent Deep Rigid MRI Motion Correction
Nalini M. Singh, Neel Dey, Malte Hoffmann +5
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies. Current retrospective rigid intra-slice motion…
Automatic Segmentation of the Placenta in BOLD MRI Time Series
S. Mazdak Abulnaga, Sean I. Young, Katherine Hobgood +5
Blood oxygen level dependent (BOLD) MRI with maternal hyperoxia can assess oxygen transport within the placenta and has emerged as a promising tool to study placental function. Mea…
SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI
Junshen Xu, Daniel Moyer, P. Ellen Grant +3
Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task…
Supervision by Denoising for Medical Image Segmentation
Sean I. Young, Adrian V. Dalca, Enzo Ferrante +4
Learning-based image reconstruction models, such as those based on the U-Net, require a large set of labeled images if good generalization is to be guaranteed. In some imaging doma…
Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels
Sungmin Hong, Anna K. Bonkhoff, Andrew Hoopes +6
Despite the superior performance of Deep Learning (DL) on numerous segmentation tasks, the DL-based approaches are notoriously overconfident about their prediction with highly pola…
3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images
Sungmin Hong, Razvan Marinescu, Adrian V. Dalca +4
Image synthesis via Generative Adversarial Networks (GANs) of three-dimensional (3D) medical images has great potential that can be extended to many medical applications, such as,…
STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning
Junshen Xu, Esra Abaci Turk, P. Ellen Grant +2
Fetal motion is unpredictable and rapid on the scale of conventional MR scan times. Therefore, dynamic fetal MRI, which aims at capturing fetal motion and dynamics of fetal functio…
Segmentation of Anatomical Layers and Artifacts in Intravascular Polarization Sensitive Optical Coherence Tomography Using Attending Physician and Boundary Cardinality Losses
Mohammad Haft-Javaherian, Martin Villiger, Kenichiro Otsuka +4
Intravascular ultrasound and optical coherence tomography are widely available for characterizing coronary stenoses and provide critical vessel parameters to optimize percutaneous…
Multimodal Representation Learning via Maximization of Local Mutual Information
Ruizhi Liao, Daniel Moyer, Miriam Cha +5
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn…
Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast
Juan Eugenio Iglesias, Benjamin Billot, Yael Balbastre +6
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently hav…
Deep Learning to Quantify Pulmonary Edema in Chest Radiographs
Steven Horng, Ruizhi Liao, Xin Wang +3
Purpose: To develop a machine learning model to classify the severity grades of pulmonary edema on chest radiographs. Materials and Methods: In this retrospective study, 369,071 ch…
Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency
Junshen Xu, Sayeri Lala, Borjan Gagoski +4
Fetal brain MRI is useful for diagnosing brain abnormalities but is challenged by fetal motion. The current protocol for T2-weighted fetal brain MRI is not robust to motion so imag…
Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators
Bernhard Egger, Markus D. Schirmer, Florian Dubost +3
We propose and demonstrate a joint model of anatomical shapes, image features and clinical indicators for statistical shape modeling and medical image analysis. The key idea is to…
Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network
Junshen Xu, Molin Zhang, Esra Abaci Turk +5
The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion. Motion of the fetus, which is unpredictable an…