Multilevel comparison of deep learning models for function quantification in cardiovascular magnetic resonance: On the redundancy of architectural variations

Article Biology

Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection

Elham Avard et al.

Summary: This study demonstrates that radiomics analysis on non-contrast Cine-CMR images can accurately differentiate between myocardial infarction (MI) and normal tissue, potentially serving as an alternative diagnostic method.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Add to Collection

Article Cardiac & Cardiovascular Systems

Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis

Avan Suinesiaputra et al.

Summary: The study aimed to develop a fully automated CMR analysis pipeline that leverages machine learning algorithms to extract additional information from a large-scale legacy dataset. The results showed that the proposed pipeline not only enables the automated assessment of additional measures of ventricular function but also allows the study of relationships between cardiac morphologies and future cardiac events in a large-scale population study.

FRONTIERS IN CARDIOVASCULAR MEDICINE (2022)

Add to Collection

Article Multidisciplinary Sciences

Introduction of Lazy Luna an automatic software-driven multilevel comparison of ventricular function quantification in cardiovascular magnetic resonance imaging

Thomas Hadler et al.

Summary: This study introduces software capable of automatic multilevel comparison and analysis for cardiac function assessment. The software demonstrates good reader agreement in clinical results and segmentation metrics. It also provides automated spreadsheets and figures to visualize metric values and differences in results. Multilevel comparison is feasible and can be extended to other cardiac structures in the future.

SCIENTIFIC REPORTS (2022)

Add to Collection

Review Chemistry, Multidisciplinary

From Accuracy to Reliability and Robustness in Cardiac Magnetic Resonance Image Segmentation: A Review

Francesco Galati et al.

Summary: Since the rise of deep learning in the mid-2010s, cardiac magnetic resonance image segmentation has made significant progress. However, the reliability and robustness of deep learning segmentation models still need attention. This paper studies the accuracy evolution of CMR segmentation, defines reliability and robustness, and identifies factors that limit the reliability and robustness of current deep learning CMR segmentation techniques. The paper also provides an overview of works focused on improving reliability and robustness, categorizing them into quality control methods and model improvement techniques.

APPLIED SCIENCES-BASEL (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology

James R. Clough et al.

Summary: This method introduces prior knowledge about the topology of segmented objects into the training process of neural networks for image or volume segmentation. The use of persistent homology allows for specifying desired topological features and driving the proposed segmentations to contain these features. The experiments demonstrate the effectiveness of embedding explicit prior knowledge in challenging segmentation tasks.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

Add to Collection

Article Cardiac & Cardiovascular Systems

Fast acquisition of left and right ventricular function parameters applying cardiovascular magnetic resonance in clinical routine - validation of a 2-shot compressed sensing cine sequence

Jan Groeschel et al.

Summary: Cine sequences accelerated by compressed sensing are feasible and effective for acquiring cardiac morphology in less time with equivalent image quality. The scan time is significantly shorter compared to conventional methods.

SCANDINAVIAN CARDIOVASCULAR JOURNAL (2022)

Add to Collection

Review Cardiac & Cardiovascular Systems

Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging

Ernst Wellnhofer

Summary: Recent advancements in digital health data recording, computing power, and artificial intelligence methods are poised to disrupt technology in medicine. These advancements offer potential benefits but also raise challenges regarding regulation, transparency, and data quality for machine learning algorithms. Balancing innovation and regulation is key, with a risk-based regulatory approach needed to address emerging risks and ensure ethical use of AI in the medical field.

FRONTIERS IN CARDIOVASCULAR MEDICINE (2022)

Add to Collection

Article Cardiac & Cardiovascular Systems

Automated Quality-Controlled Cardiovascular Magnetic Resonance Pericardial Fat Quantification Using a Convolutional Neural Network in the UK Biobank

Andrew Bard et al.

Summary: The study developed a fully automated CMR PAT quantification method, which showed good performance on independent and external datasets, high correlation with reference standard CCT PAT measurement, and expected clinical associations with diabetes.

FRONTIERS IN CARDIOVASCULAR MEDICINE (2021)

Add to Collection

Article Biochemical Research Methods

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

Fabian Isensee et al.

Summary: nnU-Net is a deep learning-based image segmentation method that automatically configures itself for diverse biological and medical image segmentation tasks, offering state-of-the-art performance as an out-of-the-box tool.

NATURE METHODS (2021)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

Victor M. Campello et al.

Summary: The emergence of deep learning has advanced cardiac magnetic resonance segmentation, yet current models lack generalizability. A recent competition emphasized the importance of data augmentation in training deep learning models and provided new data resources for future research.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2021)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Disentangle, Align and Fuse for Multimodal and Semi-Supervised Image Segmentation

Agisilaos Chartsias et al.

Summary: Magnetic resonance protocols rely on multiple sequences for evaluating pathology and organ status. Utilizing shared anatomical information between modalities can improve multi-modality processing and learning. Our method enhances segmentation accuracy of the modality of interest, even with limited or no annotations, by leveraging information from other modalities.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2021)

Add to Collection

Review Radiology, Nuclear Medicine & Medical Imaging

Bringing AI to the clinic: blueprint for a vendor-neutral AI deployment infrastructure

Tim Leiner et al.

Summary: AI offers significant potential for improving patient care, but actual implementation in real-world settings is currently limited. One key challenge is the lack of vendor-neutral infrastructure for deployment. Successful integration of AI into clinical practice requires close collaboration among radiologists, data scientists, software developers, hospital IT experts, and patient involvement.

INSIGHTS INTO IMAGING (2021)

Add to Collection

Article Cardiac & Cardiovascular Systems

Cardiac Magnetic Resonance Radiomics Reveal Differential Impact of Sex, Age, and Vascular Risk Factors on Cardiac Structure and Myocardial Tissue

Zahra Raisi-Estabragh et al.

Summary: This study found that among healthy individuals, men tend to have larger ventricles with dimmer and less complex myocardium compared to women. Increasing age was associated with smaller ventricles and greater variation in myocardial intensities. Additionally, vascular risk factors were associated with specific changes in CMR radiomics features, with diabetes and high cholesterol having a greater impact on ventricular size in men than women.

FRONTIERS IN CARDIOVASCULAR MEDICINE (2021)

Add to Collection

Article Cardiac & Cardiovascular Systems

2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure

Theresa A. McDonagh et al.

EUROPEAN HEART JOURNAL (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation

Nabil Ibtehaz et al.

NEURAL NETWORKS (2020)

Add to Collection

Article Cardiac & Cardiovascular Systems

Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images

Chen Chen et al.

FRONTIERS IN CARDIOVASCULAR MEDICINE (2020)

Add to Collection

Editorial Material Cardiac & Cardiovascular Systems

Editorial: Current and Future Role of Artificial Intelligence in Cardiac Imaging

Karim Lekadir et al.

FRONTIERS IN CARDIOVASCULAR MEDICINE (2020)

Add to Collection

Article Radiology, Nuclear Medicine & Medical Imaging

Regulatory Frameworks for Development and Evaluation of Artificial Intelligence–Based Diagnostic Imaging Algorithms: Summary and Recommendations

David B. Larson et al.

Journal of the American College of Radiology (2020)

Add to Collection

Article Multidisciplinary Sciences

Automatic segmentation with detection of local segmentation failures in cardiac MRI

Jorg Sander et al.

SCIENTIFIC REPORTS (2020)

Add to Collection

Article Cardiac & Cardiovascular Systems

Standardized image interpretation and post-processing in cardiovascular magnetic resonance-2020 update Society for Cardiovascular Magnetic Resonance (SCMR): Board of Trustees Task Force on Standardized Post-Processing

Jeanette Schulz-Menger et al.

JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE (2020)

Add to Collection

Review Cardiac & Cardiovascular Systems

Deep Learning for Cardiac Image Segmentation: A Review

Chen Chen et al.

FRONTIERS IN CARDIOVASCULAR MEDICINE (2020)

Add to Collection

Article Cardiac & Cardiovascular Systems

Myocardial Tissue Characterization and Fibrosis by Imaging

Theodoros D. Karamitsos et al.

JACC-Cardiovascular Imaging (2019)

Add to Collection

Article Computer Science, Information Systems

Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation

Clement Zotti et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2019)

Add to Collection

Article Radiology, Nuclear Medicine & Medical Imaging

Deep Learning-based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study

Qian Tao et al.

RADIOLOGY (2019)

Add to Collection

Article Cardiac & Cardiovascular Systems

Quantification in cardiovascular magnetic resonance: agreement of software from three different vendors on assessment of left ventricular function, 2D flow and parametric mapping

Leonora Zange et al.

JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE (2019)

Add to Collection

Proceedings Paper Cardiac & Cardiovascular Systems

FR-Net: Joint Reconstruction and Segmentation in Compressed Sensing Cardiac MRI

Qiaoying Huang et al.

FUNCTIONAL IMAGING AND MODELING OF THE HEART, FIMH 2019 (2019)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Olivier Bernard et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2018)

Add to Collection

Review Cardiac & Cardiovascular Systems

Cardiovascular Magnetic Resonance in Nonischemic Myocardial Inflammation Expert Recommendations

Vanessa M. Ferreira et al.

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (2018)

Add to Collection

Article Cardiac & Cardiovascular Systems

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Wenjia Bai et al.

JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE (2018)

Add to Collection

Article Computer Science, Artificial Intelligence

A survey on deep learning in medical image analysis

Geert Litjens et al.

MEDICAL IMAGE ANALYSIS (2017)

Add to Collection

Article Cardiac & Cardiovascular Systems

Representation of cardiovascular magnetic resonance in the AHA / ACC guidelines

Florian von Knobelsdorff-Brenkenhoff et al.

JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE (2017)

Add to Collection

Article Radiology, Nuclear Medicine & Medical Imaging

The Effects of Changes in Utilization and Technological Advancements Of Cross-Sectional Imaging on Radiologist Workload

Robert J. McDonald et al.

ACADEMIC RADIOLOGY (2015)

Add to Collection

Review Cardiac & Cardiovascular Systems

The Role of Cardiovascular Magnetic Resonance Imaging in Heart Failure

Theodoros D. Karamitsos et al.

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (2009)

Add to Collection

Multilevel comparison of deep learning models for function quantification in cardiovascular magnetic resonance: On the redundancy of architectural variations

Related references

Export Citation

Share Paper