output
20152025
most citedUnbiased split variable selection for random survival forests using maximally selected rank statistics

150 citations

Showing eess.IVShow all

6 papers · 1 filter

eess.IV2024★ 8 cited

Real-time guidewire tracking and segmentation in intraoperative x-ray

Baochang Zhang, Mai Bui, Cheng Wang +3

During endovascular interventions, physicians have to perform accurate and immediate operations based on the available real-time information, such as the shape and position of guid…

eess.IV2023★ 20 cited

Assessment of Deep Learning Segmentation for Real-Time Free-Breathing Cardiac Magnetic Resonance Imaging at Rest and Under Exercise Stress

Martin Schilling, Christina Unterberg-Buchwald, Joachim Lotz +1

In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under…

eess.IV2023★ 2 cited

Generative Priors for MRI Reconstruction Trained from Magnitude-Only Images Using Phase Augmentation

Guanxiong Luo, Xiaoqing Wang, Mortiz Blumenthal +5

Purpose: In this work, we present a workflow to construct generic and robust generative image priors from magnitude-only images. The priors can then be used for regularization in r…

eess.IV2021★ 16 cited

Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks

Sven Koehler, Tarique Hussain, Zach Blair +9

Anisotropic multi-slice Cardiac Magnetic Resonance (CMR) Images are conventionally acquired in patient-specific short-axis (SAX) orientation. In specific cardiovascular diseases th…

eess.IV2020★ 37 cited

Physics-based Reconstruction Methods for Magnetic Resonance Imaging

Xiaoqing Wang, Zhengguo Tan, Nick Scholand +2

Conventional Magnetic Resonance Imaging (MRI) is hampered by long scan times and only qualitative image contrasts that prohibit a direct comparison between different systems. To ad…

eess.IV2020★ 36 cited

CG-SENSE revisited: Results from the first ISMRM reproducibility challenge

Oliver Maier, Steven H. Baete, Alexander Fyrdahl +11

Purpose: The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of…