Search Results for author:
Found 8 papers, 3 papers with code
IVIM-Morph: Motion-compensated quantitative Intra-voxel Incoherent Motion (IVIM) analysis for functional fetal lung maturity assessment from diffusion-weighted MRI data
IVIM-morph combines two sub-networks, a registration sub-network, and an IVIM model fitting sub-network, enabling simultaneous estimation of IVIM model parameters and motion.
Masked Conditional Diffusion Models for Image Analysis with Application to Radiographic Diagnosis of Infant Abuse
Unlike previous models that fail to generate data that span the diverse radiographic appearance of the distal tibial CML, our proposed masked conditional diffusion model (MaC-DM) not only generates realistic-appearing and wide-ranging synthetic images of the distal tibial radiographs with and without CMLs, it also generates their associated segmentation labels.
qDWI-Morph: Motion-compensated quantitative Diffusion-Weighted MRI analysis for fetal lung maturity assessment
Our approach couples a registration sub-network with a quantitative DWI model fitting sub-network.
SUPER-IVIM-DC: Intra-voxel incoherent motion based Fetal lung maturity assessment from limited DWI data using supervised learning coupled with data-consistency
SUPER-IVIM-DC has the potential to reduce the long acquisition times associated with IVIM analysis of DWI data and to provide clinically feasible bio-markers for non-invasive fetal lung maturity assessment.
CORPS: Cost-free Rigorous Pseudo-labeling based on Similarity-ranking for Brain MRI Segmentation
With this motivation, we propose CORPS, a semi-supervised segmentation framework built upon a novel atlas-based pseudo-labeling method and a 3D deep convolutional neural network (DCNN) for 3D brain MRI segmentation.
Learning the Regularization in DCE-MR Image Reconstruction for Functional Imaging of Kidneys
At the same time, the proposed approach reduces the artifacts in the reconstructed images.
Weakly Supervised Segmentation by A Deep Geodesic Prior
To alleviate this limitation, in this study, we propose a weakly supervised image segmentation method based on a deep geodesic prior.
Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks
In this paper, we propose a time and memory efficient fully automated segmentation method which achieves high segmentation accuracy with running time in the order of seconds in both normal kidneys and kidneys with hydronephrosis.
