10 Result(s)
within
Chapter
Steffen E. Petersen
Computer Science
Health Informatics
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Chapter
AI in the Real World
This chapter deals with important considerations to factor in when translating technical advances in AI to real clinical workflows. The importance of considering existing workflows is emphasized, including ide...
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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
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
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
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 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
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...
