353 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
AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning
Perfusion imaging is crucial in acute ischemic stroke for quantifying the
salvageable penumbra and irreversibly damaged core lesions. As such, it helps
clinicians to decide on the optimal reperfusion treatment. In perfusion CT
imaging, deconvolution methods are used to obtain clinically interpretable
perfusion parameters that allow identifying brain tissue abnormalities.
Deconvolution methods require the selection of two reference vascular functions
as inputs to the model: the arterial input function (AIF) and the venous output
function, with the AIF as the most critical model input. When manually
performed, the vascular function selection is time demanding, suffers from poor
reproducibility and is subject to the professionals' experience. This leads to
potentially unreliable quantification of the penumbra and core lesions and,
hence, might harm the treatment decision process. In this work we automatize
the perfusion analysis with AIFNet, a fully automatic and end-to-end trainable
deep learning approach for estimating the vascular functions. Unlike previous
methods using clustering or segmentation techniques to select vascular voxels,
AIFNet is directly optimized at the vascular function estimation, which allows
to better recognise the time-curve profiles. Validation on the public ISLES18
stroke database shows that AIFNet reaches inter-rater performance for the
vascular function estimation and, subsequently, for the parameter maps and core
lesion quantification obtained through deconvolution. We conclude that AIFNet
has potential for clinical transfer and could be incorporated in perfusion
deconvolution software.Comment: Preprint submitted to Elsevie
Detection of the arterial input function using DSC-MRI data
Accurate detection of arterial input function is a crucial step in obtaining perfusion hemodynamic parameters using dynamic susceptibility contrast-enhanced magnetic resonance imaging. It is required as input for perfusion quantification and has a great impact on the result of the deconvolution operation. To improve the reproducibility and reliability of arterial input function detection, several semi- or fully automatic methods have been proposed. This study provides an overview of the current state of the field of arterial input function detection. Methods most commonly used for semi- and fully automatic arterial input function detection are reviewed, and their advantages and disadvantages are listed
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation
Image segmentation is a fundamental and challenging problem in computer
vision with applications spanning multiple areas, such as medical imaging,
remote sensing, and autonomous vehicles. Recently, convolutional neural
networks (CNNs) have gained traction in the design of automated segmentation
pipelines. Although CNN-based models are adept at learning abstract features
from raw image data, their performance is dependent on the availability and
size of suitable training datasets. Additionally, these models are often unable
to capture the details of object boundaries and generalize poorly to unseen
classes. In this thesis, we devise novel methodologies that address these
issues and establish robust representation learning frameworks for
fully-automatic semantic segmentation in medical imaging and mainstream
computer vision. In particular, our contributions include (1) state-of-the-art
2D and 3D image segmentation networks for computer vision and medical image
analysis, (2) an end-to-end trainable image segmentation framework that unifies
CNNs and active contour models with learnable parameters for fast and robust
object delineation, (3) a novel approach for disentangling edge and texture
processing in segmentation networks, and (4) a novel few-shot learning model in
both supervised settings and semi-supervised settings where synergies between
latent and image spaces are leveraged to learn to segment images given limited
training data.Comment: PhD dissertation, UCLA, 202
Core and penumbra estimation using deep learning-based AIF in association with clinical measures in computed tomography perfusion (CTP)
Objectives To investigate whether utilizing a convolutional neural network (CNN)-based arterial input function (AIF) improves the volumetric estimation of core and penumbra in association with clinical measures in stroke patients. Methods The study included 160 acute ischemic stroke patients (male = 87, female = 73, median age = 73 years) with approval from the institutional review board. The patients had undergone CTP imaging, NIHSS and ASPECTS grading. convolutional neural network (CNN) model was trained to fit a raw AIF curve to a gamma variate function. CNN AIF was utilized to estimate the core and penumbra volumes which were further validated with clinical scores. Results Penumbra estimated by CNN AIF correlated positively with the NIHSS score (r = 0.69; p 20) and lower ASPECT score ( 10 s, Tmax > 10 s volumes were statistically significantly higher (p < .05). Conclusions With inclusion of the CNN AIF in perfusion imaging pipeline, penumbra and core estimations are more reliable as they correlate with scores representing neurological deficits in stroke. Critical relevance statement With CNN AIF perfusion imaging pipeline, penumbra and core estimations are more reliable as they correlate with scores representing neurological deficits in stroke
Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning
Recent development of quantitative myocardial blood flow (MBF) mapping allows
direct evaluation of absolute myocardial perfusion, by computing pixel-wise
flow maps. Clinical studies suggest quantitative evaluation would be more
desirable for objectivity and efficiency. Objective assessment can be further
facilitated by segmenting the myocardium and automatically generating reports
following the AHA model. This will free user interaction for analysis and lead
to a 'one-click' solution to improve workflow. This paper proposes a deep
neural network based computational workflow for inline myocardial perfusion
analysis. Adenosine stress and rest perfusion scans were acquired from three
hospitals. Training set included N=1,825 perfusion series from 1,034 patients.
Independent test set included 200 scans from 105 patients. Data were
consecutively acquired at each site. A convolution neural net (CNN) model was
trained to provide segmentation for LV cavity, myocardium and right ventricular
by processing incoming 2D+T perfusion Gd series. Model outputs were compared to
manual ground-truth for accuracy of segmentation and flow measures derived on
global and per-sector basis. The trained models were integrated onto MR
scanners for effective inference. Segmentation accuracy and myocardial flow
measures were compared between CNN models and manual ground-truth. The mean
Dice ratio of CNN derived myocardium was 0.93 +/- 0.04. Both global flow and
per-sector values showed no significant difference, compared to manual results.
The AHA 16 segment model was automatically generated and reported on the MR
scanner. As a result, the fully automated analysis of perfusion flow mapping
was achieved. This solution was integrated on the MR scanner, enabling
'one-click' analysis and reporting of myocardial blood flow.Comment: This work has been submitted to Radiology: Artificial Intelligence
for possible publicatio
Image Quality Assessment for Population Cardiac MRI: From Detection to Synthesis
Cardiac magnetic resonance (CMR) images play a growing role in diagnostic imaging of cardiovascular diseases. Left Ventricular (LV) cardiac anatomy and function are widely used for diagnosis and monitoring disease progression in cardiology and to assess the patient's response to cardiac surgery and interventional procedures. For population imaging studies, CMR is arguably the most comprehensive imaging modality for non-invasive and non-ionising imaging of the heart and great vessels and, hence, most suited for population imaging cohorts. Due to insufficient radiographer's experience in planning a scan, natural cardiac muscle contraction, breathing motion, and imperfect triggering, CMR can display incomplete LV coverage, which hampers quantitative LV characterization and diagnostic accuracy.
To tackle this limitation and enhance the accuracy and robustness of the automated cardiac volume and functional assessment, this thesis focuses on the development and application of state-of-the-art deep learning (DL) techniques in cardiac imaging. Specifically, we propose new image feature representation types that are learnt with DL models and aimed at highlighting the CMR image quality cross-dataset. These representations are also intended to estimate the CMR image quality for better interpretation and analysis. Moreover, we investigate how quantitative analysis can benefit when these learnt image representations are used in image synthesis.
Specifically, a 3D fisher discriminative representation is introduced to identify CMR image quality in the UK Biobank cardiac data. Additionally, a novel adversarial learning (AL) framework is introduced for the cross-dataset CMR image quality assessment and we show that the common representations learnt by AL can be useful and informative for cross-dataset CMR image analysis. Moreover, we utilize the dataset invariance (DI) representations for CMR volumes interpolation by introducing a novel generative adversarial nets (GANs) based image synthesis framework, which enhance the CMR image quality cross-dataset
Machine Learning for Biomedical Application
Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images
- …