116 citations · 185 across the 12 of their papers we have counts for
13 papers
Large-scale modality-invariant foundation models for brain MRI analysis: Application to lesion segmentation
Petros Koutsouvelis, Matej Gazda, Leroy Volmer +5
The field of computer vision is undergoing a paradigm shift toward large-scale foundation model pre-training via self-supervised learning (SSL). Leveraging large volumes of unlabel…
Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
Kang Wang, Chen Qin, Zhang Shi +46
Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on…
Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensembles
Evi M. C. Huijben, Sina Amirrajab, Josien P. W. Pluim
Out-of-distribution (OOD) detection is crucial for safely deploying automated medical image analysis systems, as abnormal patterns in images could hamper their performance. However…
WAND: Wavelet Analysis-based Neural Decomposition of MRS Signals for Artifact Removal
Julian P. Merkofer, Dennis M. J. van de Sande, Sina Amirrajab +3
Accurate quantification of metabolites in magnetic resonance spectroscopy (MRS) is challenged by low signal-to-noise ratio (SNR), overlapping metabolites, and various artifacts. Pa…
Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
Mahmoud Ibrahim, Yasmina Al Khalil, Sina Amirrajab +6
This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic…
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection
Evi M. C. Huijben, Sina Amirrajab, Josien P. W. Pluim
Out-of-distribution (OOD) detection is crucial for the safety and reliability of artificial intelligence algorithms, especially in the medical domain. In the context of the Medical…
A Deep Learning Approach Utilizing Covariance Matrix Analysis for the ISBI Edited MRS Reconstruction Challenge
Julian P. Merkofer, Dennis M. J. van de Sande, Sina Amirrajab +5
This work proposes a method to accelerate the acquisition of high-quality edited magnetic resonance spectroscopy (MRS) scans using machine learning models taking the sample covaria…
A Review of Machine Learning Applications for the Proton Magnetic Resonance Spectroscopy Workflow
Dennis M. J. van de Sande, Julian P. Merkofer, Sina Amirrajab +6
This literature review presents a comprehensive overview of machine learning (ML) applications in proton magnetic resonance spectroscopy (MRS). As the use of ML techniques in MRS c…
Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs
Sina Amirrajab, Cristian Lorenz, Juergen Weese +2
We propose a method for synthesizing cardiac magnetic resonance (MR) images with plausible heart pathologies and realistic appearances for the purpose of generating labeled data fo…
sim2real: Cardiac MR Image Simulation-to-Real Translation via Unsupervised GANs
Sina Amirrajab, Yasmina Al Khalil, Cristian Lorenz +3
There has been considerable interest in the MR physics-based simulation of a database of virtual cardiac MR images for the development of deep-learning analysis networks. However,…
Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data Augmentation
Didier R. P. R. M. Lustermans, Sina Amirrajab, Mitko Veta +2
Background: The clinical utility of late gadolinium enhancement (LGE) cardiac MRI is limited by the lack of standardization, and time-consuming postprocessing. In this work, we tes…
XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms
Sina Amirrajab, Samaneh Abbasi-Sureshjani, Yasmina Al Khalil +4
Generative adversarial networks (GANs) have provided promising data enrichment solutions by synthesizing high-fidelity images. However, generating large sets of labeled images with…
4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model
Samaneh Abbasi-Sureshjani, Sina Amirrajab, Cristian Lorenz +3
We propose a hybrid controllable image generation method to synthesize anatomically meaningful 3D+t labeled Cardiac Magnetic Resonance (CMR) images. Our hybrid method takes the mec…