A review of medical image data augmentation techniques for deep learning applications
Research in artificial intelligence for radiology and radiotherapy has recently become
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
Going deep in medical image analysis: concepts, methods, challenges, and future directions
Medical image analysis is currently experiencing a paradigm shift due to deep learning. This
technology has recently attracted so much interest of the Medical Imaging Community that it …
technology has recently attracted so much interest of the Medical Imaging Community that it …
Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function
Objectives This study sought to develop a fully automated framework for cardiac function
analysis from cardiac magnetic resonance (CMR), including comprehensive quality control …
analysis from cardiac magnetic resonance (CMR), including comprehensive quality control …
Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …
[HTML][HTML] Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions
Head motion during MRI acquisition presents significant challenges for neuroimaging
analyses. In this work, we present a retrospective motion correction framework built on a …
analyses. In this work, we present a retrospective motion correction framework built on a …
[HTML][HTML] Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning
Good quality of medical images is a prerequisite for the success of subsequent image
analysis pipelines. Quality assessment of medical images is therefore an essential activity …
analysis pipelines. Quality assessment of medical images is therefore an essential activity …
Deep learning‐based motion quantification from k‐space for fast model‐based magnetic resonance imaging motion correction
Background Intra‐scan rigid‐body motion is a costly and ubiquitous problem in clinical
magnetic resonance imaging (MRI) of the head. Purpose State‐of‐the‐art methods for …
magnetic resonance imaging (MRI) of the head. Purpose State‐of‐the‐art methods for …
Model-based and data-driven strategies in medical image computing
D Rueckert, JA Schnabel - Proceedings of the IEEE, 2019 - ieeexplore.ieee.org
Model-based approaches for image reconstruction, analysis, and interpretation have made
significant progress over the past decades. Many of these approaches are based on either …
significant progress over the past decades. Many of these approaches are based on either …
[HTML][HTML] Deep learning in spatiotemporal cardiac imaging: A review of methodologies and clinical usability
KAL Hernandez, T Rienmüller, D Baumgartner… - Computers in Biology …, 2021 - Elsevier
The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the
visualization and interpretation of altered morphological structures and function of the heart …
visualization and interpretation of altered morphological structures and function of the heart …
Detection and correction of cardiac MRI motion artefacts during reconstruction from k-space
In fully sampled cardiac MR (CMR) acquisitions, motion can lead to corruption of k-space
lines, which can result in artefacts in the reconstructed images. In this paper, we propose a …
lines, which can result in artefacts in the reconstructed images. In this paper, we propose a …