Applications of artificial intelligence in cardiovascular imaging
M Sermesant, H Delingette, H Cochet, P Jaïs… - Nature Reviews …, 2021 - nature.com
Research into artificial intelligence (AI) has made tremendous progress over the past
decade. In particular, the AI-powered analysis of images and signals has reached human …
decade. In particular, the AI-powered analysis of images and signals has reached human …
Multiparametric cardiovascular magnetic resonance approach in diagnosing, monitoring, and prognostication of myocarditis
C Eichhorn, S Greulich, C Bucciarelli-Ducci… - Cardiovascular …, 2022 - jacc.org
Myocarditis represents the entity of an inflamed myocardium and is a diagnostic challenge
caused by its heterogeneous presentation. Contemporary noninvasive evaluation of patients …
caused by its heterogeneous presentation. Contemporary noninvasive evaluation of patients …
Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge
The emergence of deep learning has considerably advanced the state-of-the-art in cardiac
magnetic resonance (CMR) segmentation. Many techniques have been proposed over the …
magnetic resonance (CMR) segmentation. Many techniques have been proposed over the …
Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers
M Khened, VA Kollerathu, G Krishnamurthi - Medical image analysis, 2019 - Elsevier
Deep fully convolutional neural network (FCN) based architectures have shown great
potential in medical image segmentation. However, such architectures usually have millions …
potential in medical image segmentation. However, such architectures usually have millions …
Improving the generalizability of convolutional neural network-based segmentation on CMR images
Background: Convolutional neural network (CNN) based segmentation methods provide an
efficient and automated way for clinicians to assess the structure and function of the heart in …
efficient and automated way for clinicians to assess the structure and function of the heart in …
Automated segmentation of tissues using CT and MRI: a systematic review
Rationale and Objectives The automated segmentation of organs and tissues throughout the
body using computed tomography and magnetic resonance imaging has been rapidly …
body using computed tomography and magnetic resonance imaging has been rapidly …
Deep learning-based detection and correction of cardiac MR motion artefacts during reconstruction for high-quality segmentation
Segmenting anatomical structures in medical images has been successfully addressed with
deep learning methods for a range of applications. However, this success is heavily …
deep learning methods for a range of applications. However, this success is heavily …
Automatic segmentation with detection of local segmentation failures in cardiac MRI
Segmentation of cardiac anatomical structures in cardiac magnetic resonance images
(CMRI) is a prerequisite for automatic diagnosis and prognosis of cardiovascular diseases …
(CMRI) is a prerequisite for automatic diagnosis and prognosis of cardiovascular diseases …
Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse
Many studies on machine learning (ML) for computer-aided diagnosis have so far been
mostly restricted to high-quality research data. Clinical data warehouses, gathering routine …
mostly restricted to high-quality research data. Clinical data warehouses, gathering routine …
Biomedical image segmentation: a survey
Y Alzahrani, B Boufama - SN Computer Science, 2021 - Springer
Abstract Medical Image Segmentation is the process of segmenting and detecting
boundaries of anatomical structures in various types of 2D and 3D-medical images. The …
boundaries of anatomical structures in various types of 2D and 3D-medical images. The …