Autofocusing+: noise-resilient motion correction in magnetic resonance imaging

E Kuzmina, A Razumov, OY Rogov… - … Conference on Medical …, 2022 - Springer
Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance
Imaging (MRI). In this work, we propose a neural network-based regularization term to …

Generating synthetic multispectral images for semantic segmentation in forestry applications

D Bittner, JF Ferreira, ME Andrada, JJ Bird, D Portugal - 2022 - irep.ntu.ac.uk
In this paper, we introduce a GAN-based solution for generating synthetic multispectral
images from fully-annotated RGB images for data augmentation purposes in forestry …

Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation

A Ranem, J Kalkhof, C Özer, A Mukhopadhyay… - … Workshop on Statistical …, 2022 - Springer
While machine learning approaches perform well on their training domain, they generally
tend to fail in a real-world application. In cardiovascular magnetic resonance imaging …

Automatic Quality Assessment of Cardiac MR Images with Motion Artefacts Using Multi-task Learning and K-Space Motion Artefact Augmentation

TW Arega, S Bricq, F Meriaudeau - … and Computational Models of the Heart, 2022 - Springer
The movement of patients and respiratory motion during MRI acquisition produce image
artefacts that reduce the image quality and its diagnostic value. Quality assessment of the …

Deep learning augmentation for medical image analysis

F Altaf - 2022 - ro.ecu.edu.au
Deep learning is at the center of the current rise of computer aided diagnosis in medical
imaging. This technology has the ability to mimic extremely complex mathematical functions …

[PDF][PDF] Machine Learning Based Cardiac Scar Detection in Computed Tomography

H O'Brien, S Niederer, K Rhode - 2022 - kclpure.kcl.ac.uk
Identifying cardiac patients with scar tissue is important for assisting diagnosis and guiding
interventions. Late gadolinium enhancement (LGE) MRI is the gold standard for scar …