16 Result(s)
within
Steffen E. Petersen
Image Processing and Computer Vision
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Chapter and Conference Paper
Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation
The subject of ‘fairness’ in artificial intelligence (AI) refers to assessing AI algorithms for potential bias based on demographic characteristics such as race and gender, and the development of algorithms to...
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Chapter and Conference Paper
Multi-modal Brain Age Estimation: A Comparative Study Confirms the Importance of Microstructure
Brain age inferred from neuroimaging data could reveal important information about the evolution of structural and functional cerebral features across the life span. This has important implications for underst...
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Chapter and Conference Paper
Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI
Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue. However, while there are ma...
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Chapter and Conference Paper
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regressi...
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Chapter and Conference Paper
End-Diastolic and End-Systolic LV Morphology in the Presence of Cardiovascular Risk Factors: A UK Biobank Study
Left ventricular function and morphology have been shown to be important factors in clinical and pre-clinical cardiovascular disease. In this paper we used atlas-based techniques to capture the full extent of...
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Chapter and Conference Paper
3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata
Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approach, fully automatic large-scal...
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Chapter and Conference Paper
Unsupervised Standard Plane Synthesis in Population Cine MRI via Cycle-Consistent Adversarial Networks
In clinical studies or population imaging settings, cardiac magnetic resonance (CMR) images may suffer from artifacts due to variability in the breath-hold position adopted by the patient during the scan. Cons...
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Chapter and Conference Paper
High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort
The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imag...
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Chapter and Conference Paper
Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging
Recent progress in fully-automated image segmentation has enabled efficient extraction of clinical parameters in large-scale clinical imaging studies, reducing laborious manual processing. However, the current...
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Chapter
Image Quality Assessment for Population Cardiac Magnetic Resonance Imaging
Cardiac magnetic resonance (CMR) images play a growing role in diagnostic imaging of cardiovascular diseases. is arguably the most comprehensive imaging modality for noninvasive and nonionizing imaging of th...
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Chapter and Conference Paper
Real-Time Prediction of Segmentation Quality
Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due to low image quality, artifa...
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Chapter and Conference Paper
Multi-Input and Dataset-Invariant Adversarial Learning (MDAL) for Left and Right-Ventricular Coverage Estimation in Cardiac MRI
Cardiac functional parameters, such as, the Ejection Fraction (EF) and Cardiac Output (CO) of both ventricles, are most immediate indicators of normal/abnormal cardiac function. To compute these parameters, ac...
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Chapter and Conference Paper
Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image
Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. However, if the main purpose is t...
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Chapter and Conference Paper
A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI
Computer-aided diagnosis of cardiovascular diseases (CVDs) with cine-MRI is an important research topic to enable improved stratification of CVD patients. However, current approaches that use expert visualizat...
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Chapter and Conference Paper
Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences
Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel deep learning method for j...
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Chapter and Conference Paper
Automated Quality Assessment of Cardiac MR Images Using Convolutional Neural Networks
Image quality assessment (IQA) is crucial in large-scale population imaging so that high-throughput image analysis can extract meaningful imaging biomarkers at scale. Specifically, in this paper, we address a ...
