6 citations · 12 across the 12 of their papers we have counts for
14 papers
ARC-CT: Anatomy-Routed Contrastive Vision-Language Learning for 3D Chest CT
Huseyin Umut Isik, Mehmet Alp Ozaydin, Sila Kurugol +1
Contrastive vision-language learning uses paired chest CT volumes and radiology reports to learn abnormality classifiers without manually annotated labels. However, two characteris…
Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction
Veronika Spieker, Wenqi Huang, Cemre Ariyurek +5
Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions at high undersampling rates. Scan-specific reconstructions usin…
Gabor Primitives for Accelerated Cardiac Cine MRI Reconstruction
Wenqi Huang, Veronika Spieker, Nil Stolt-Ansó +6
Accelerated cardiac cine MRI requires reconstructing spatiotemporal images from highly undersampled k-space data. Implicit neural representations (INRs) enable scan-specific recons…
WristMIR: Coarse-to-Fine Region-Aware Retrieval of Pediatric Wrist Radiographs with Radiology Report-Driven Learning
Mert Sonmezer, Serge Vasylechko, Duygu Atasoy +2
Retrieving wrist radiographs with analogous fracture patterns is challenging because clinically important cues are subtle, highly localized and often obscured by overlapping anatom…
MRI Super-Resolution with Deep Learning: A Comprehensive Survey
Mohammad Khateri, Serge Vasylechko, Morteza Ghahremani +9
High-resolution (HR) magnetic resonance imaging (MRI) is crucial for many clinical and research applications. However, achieving it remains costly and constrained by technical trad…
An Optimized Binning and Probabilistic Slice Sharing Algorithm for Motion Correction in Abdominal DW-MRI
Michelle Su, Cemre Ariyurek, Serge Vasylechko +2
Diffusion-weighted magnetic resonance imaging (DW-MRI) is a powerful, non-invasive tool for detecting and characterizing abdominal lesions to facilitate early diagnosis, but respir…
IVIM-Morph: Motion-compensated quantitative Intra-voxel Incoherent Motion (IVIM) analysis for functional fetal lung maturity assessment from diffusion-weighted MRI data
Noga Kertes, Yael Zaffrani-Reznikov, Onur Afacan +3
Quantitative analysis of pseudo-diffusion in diffusion-weighted magnetic resonance imaging (DWI) data shows potential for assessing fetal lung maturation and generating valuable im…
Masked Conditional Diffusion Models for Image Analysis with Application to Radiographic Diagnosis of Infant Abuse
Shaoju Wu, Sila Kurugol, Andy Tsai
The classic metaphyseal lesion (CML) is a distinct injury that is highly specific for infant abuse. It commonly occurs in the distal tibia. To aid radiologists detect these subtle…
qDWI-Morph: Motion-compensated quantitative Diffusion-Weighted MRI analysis for fetal lung maturity assessment
Yael Zaffrani-Reznikov, Onur Afacan, Sila Kurugol +2
Quantitative analysis of fetal lung Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative imaging biomarkers that indirectly reflect fetal lung maturation. Ho…
SUPER-IVIM-DC: Intra-voxel incoherent motion based Fetal lung maturity assessment from limited DWI data using supervised learning coupled with data-consistency
Noam Korngut, Elad Rotman, Onur Afacan +5
Intra-voxel incoherent motion (IVIM) analysis of fetal lungs Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative imaging bio-markers that reflect, indirectl…
CORPS: Cost-free Rigorous Pseudo-labeling based on Similarity-ranking for Brain MRI Segmentation
Can Taylan Sari, Sila Kurugol, Onur Afacan +1
Segmentation of brain magnetic resonance images (MRI) is crucial for the analysis of the human brain and diagnosis of various brain disorders. The drawbacks of time-consuming and e…
Learning the Regularization in DCE-MR Image Reconstruction for Functional Imaging of Kidneys
Aziz Koçanaoğulları, Cemre Ariyurek, Onur Afacan +1
Kidney DCE-MRI aims at both qualitative assessment of kidney anatomy and quantitative assessment of kidney function by estimating the tracer kinetic (TK) model parameters. Accurate…
Weakly Supervised Segmentation by A Deep Geodesic Prior
Aliasghar Mortazi, Naji Khosravan, Drew A. Torigian +2
The performance of the state-of-the-art image segmentation methods heavily relies on the high-quality annotations, which are not easily affordable, particularly for medical data. T…
Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks
Marzieh Haghighi, Simon K. Warfield, Sila Kurugol
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of rena…