453 research outputs found
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier
Coronary artery centerline extraction in cardiac CT angiography (CCTA) images
is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We
propose an algorithm that extracts coronary artery centerlines in CCTA using a
convolutional neural network (CNN).
A 3D dilated CNN is trained to predict the most likely direction and radius
of an artery at any given point in a CCTA image based on a local image patch.
Starting from a single seed point placed manually or automatically anywhere in
a coronary artery, a tracker follows the vessel centerline in two directions
using the predictions of the CNN. Tracking is terminated when no direction can
be identified with high certainty.
The CNN was trained using 32 manually annotated centerlines in a training set
consisting of 8 CCTA images provided in the MICCAI 2008 Coronary Artery
Tracking Challenge (CAT08). Evaluation using 24 test images of the CAT08
challenge showed that extracted centerlines had an average overlap of 93.7%
with 96 manually annotated reference centerlines. Extracted centerline points
were highly accurate, with an average distance of 0.21 mm to reference
centerline points. In a second test set consisting of 50 CCTA scans, 5,448
markers in the coronary arteries were used as seed points to extract single
centerlines. This showed strong correspondence between extracted centerlines
and manually placed markers. In a third test set containing 36 CCTA scans,
fully automatic seeding and centerline extraction led to extraction of on
average 92% of clinically relevant coronary artery segments.
The proposed method is able to accurately and efficiently determine the
direction and radius of coronary arteries. The method can be trained with
limited training data, and once trained allows fast automatic or interactive
extraction of coronary artery trees from CCTA images.Comment: Accepted in Medical Image Analysi
Deep learning for automated exclusion of cardiac CT examinations negative for coronary artery calcium
Purpose: Coronary artery calcium (CAC) score has shown to be an accurate predictor of future cardiovascular events. Early detection by CAC scoring might reduce the number of deaths by cardiovascular disease (CVD). Automatically excluding scans which test negative for CAC could significantly reduce the workload of radiologists. We propose an algorithm that both excludes negative scans and segments the CAC. Method: The training and internal validation data were collected from the ROBINSCA study. The external validation data were collected from the ImaLife study. Both contain annotated low-dose non-contrast cardiac CT scans. 60 scans of participants were used for training and 2 sets of 50 CT scans of participants without CAC and 50 CT scans of participants with an Agatston score between 10 and 20 were collected for both internal and external validation. The effect of dilated convolutional layers was tested by using 2 CNN architectures. We used the patient-level accuracy as metric for assessing the accuracy of our pipeline for detection of CAC and the Dice coefficient score as metric for the segmentation of CAC. Results: Of the 50 negative cases in the internal and external validation set, 62 % and 86 % were classified correctly, respectively. There were no false negative predictions. For the segmentation task, Dice Coefficient scores of 0.63 and 0.84 were achieved for the internal and external validation datasets, respectively. Conclusions: Our algorithm excluded 86 % of all scans without CAC. Radiologists might need to spend less time on participants without CAC and could spend more time on participants that need their attention
System-Characterized Artificial Intelligence Approaches for Cardiac cellular systems and Molecular Signature analysis
The dissertation presents a significant advancement in the field of cardiac cellular systems and molecular signature systems by employing machine learning and generative artificial intelligence techniques. These methodologies are systematically characterized and applied to address critical challenges in these domains. A novel computational model is developed, which combines machine learning tools and multi-physics models. The main objective of this model is to accurately predict complex cellular dynamics, taking into account the intricate interactions within the cardiac cellular system. Furthermore, a comprehensive framework based on generative adversarial networks (GANs) is proposed. This framework is designed to generate synthetic data that faithfully represents an in-vitro cardiac cellular system. The generated data can be used to enhance the understanding and analysis of the system’s behavior. Additionally, a novel AI approach is formulated, which integrates deep learning and GAN techniques for Raman characterization. This approach enables efficient detection of multi-analyte mixtures by leveraging the power of deep learning algorithms and the generation of synthetic data through GANs. Overall, the integration of machine learning, generative artificial intelligence, and multi-physics modeling provides valuable insights and tools for precise prediction and efficient detection in cardiac cellular systems and molecular signature systems
Deep Learning from Dual-Energy Information for Whole-Heart Segmentation in Dual-Energy and Single-Energy Non-Contrast-Enhanced Cardiac CT
Deep learning-based whole-heart segmentation in coronary CT angiography
(CCTA) allows the extraction of quantitative imaging measures for
cardiovascular risk prediction. Automatic extraction of these measures in
patients undergoing only non-contrast-enhanced CT (NCCT) scanning would be
valuable. In this work, we leverage information provided by a dual-layer
detector CT scanner to obtain a reference standard in virtual non-contrast
(VNC) CT images mimicking NCCT images, and train a 3D convolutional neural
network (CNN) for the segmentation of VNC as well as NCCT images.
Contrast-enhanced acquisitions on a dual-layer detector CT scanner were
reconstructed into a CCTA and a perfectly aligned VNC image. In each CCTA
image, manual reference segmentations of the left ventricular (LV) myocardium,
LV cavity, right ventricle, left atrium, right atrium, ascending aorta, and
pulmonary artery trunk were obtained and propagated to the corresponding VNC
image. These VNC images and reference segmentations were used to train 3D CNNs
for automatic segmentation in either VNC images or NCCT images. Automatic
segmentations in VNC images showed good agreement with reference segmentations,
with an average Dice similarity coefficient of 0.897 \pm 0.034 and an average
symmetric surface distance of 1.42 \pm 0.45 mm. Volume differences [95%
confidence interval] between automatic NCCT and reference CCTA segmentations
were -19 [-67; 30] mL for LV myocardium, -25 [-78; 29] mL for LV cavity, -29
[-73; 14] mL for right ventricle, -20 [-62; 21] mL for left atrium, and -19
[-73; 34] mL for right atrium, respectively. In 214 (74%) NCCT images from an
independent multi-vendor multi-center set, two observers agreed that the
automatic segmentation was mostly accurate or better. This method might enable
quantification of additional cardiac measures from NCCT images for improved
cardiovascular risk prediction
3D Deep Learning on Medical Images: A Review
The rapid advancements in machine learning, graphics processing technologies
and availability of medical imaging data has led to a rapid increase in use of
deep learning models in the medical domain. This was exacerbated by the rapid
advancements in convolutional neural network (CNN) based architectures, which
were adopted by the medical imaging community to assist clinicians in disease
diagnosis. Since the grand success of AlexNet in 2012, CNNs have been
increasingly used in medical image analysis to improve the efficiency of human
clinicians. In recent years, three-dimensional (3D) CNNs have been employed for
analysis of medical images. In this paper, we trace the history of how the 3D
CNN was developed from its machine learning roots, give a brief mathematical
description of 3D CNN and the preprocessing steps required for medical images
before feeding them to 3D CNNs. We review the significant research in the field
of 3D medical imaging analysis using 3D CNNs (and its variants) in different
medical areas such as classification, segmentation, detection, and
localization. We conclude by discussing the challenges associated with the use
of 3D CNNs in the medical imaging domain (and the use of deep learning models,
in general) and possible future trends in the field.Comment: 13 pages, 4 figures, 2 table
Qualitative Evaluation of Common Quantitative Metrics for Clinical Acceptance of Automatic Segmentation:a Case Study on Heart Contouring from CT Images by Deep Learning Algorithms
Organs-at-risk contouring is time consuming and labour intensive. Automation by deep learning algorithms would decrease the workload of radiotherapists and technicians considerably. However, the variety of metrics used for the evaluation of deep learning algorithms make the results of many papers difficult to interpret and compare. In this paper, a qualitative evaluation is done on five established metrics to assess whether their values correlate with clinical usability. A total of 377 CT volumes with heart delineations were randomly selected for training and evaluation. A deep learning algorithm was used to predict the contours of the heart. A total of 101 CT slices from the validation set with the predicted contours were shown to three experienced radiologists. They examined each slice independently whether they would accept or adjust the prediction and if there were (small) mistakes. For each slice, the scores of this qualitative evaluation were then compared with the Sørensen-Dice coefficient (DC), the Hausdorff distance (HD), pixel-wise accuracy, sensitivity and precision. The statistical analysis of the qualitative evaluation and metrics showed a significant correlation. Of the slices with a DC over 0.96 (N = 20) or a 95% HD under 5 voxels (N = 25), no slices were rejected by the readers. Contours with lower DC or higher HD were seen in both rejected and accepted contours. Qualitative evaluation shows that it is difficult to use common quantification metrics as indicator for use in clinic. We might need to change the reporting of quantitative metrics to better reflect clinical acceptance
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