The Role Of Deep Learning In Structural And Functional Lung Imaging
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The Role Of Deep Learning In Structural And Functional Lung Imaging
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Author : Joshua Astley
language : en
Publisher:
Release Date : 2022
The Role Of Deep Learning In Structural And Functional Lung Imaging written by Joshua Astley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Pulmonary Functional Imaging
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Author : Yoshiharu Ohno
language : en
Publisher: Springer Nature
Release Date : 2020-12-11
Pulmonary Functional Imaging written by Yoshiharu Ohno and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-11 with Medical categories.
This book reviews the basics of pulmonary functional imaging using new CT and MR techniques and describes the clinical applications of these techniques in detail. The intention is to equip readers with a full understanding of pulmonary functional imaging that will allow optimal application of all relevant techniques in the assessment of a variety of diseases, including COPD, asthma, cystic fibrosis, pulmonary thromboembolism, pulmonary hypertension, lung cancer and pulmonary nodule. Pulmonary functional imaging has been promoted as a research and diagnostic tool that has the capability to overcome the limitations of morphological assessments as well as functional evaluation based on traditional nuclear medicine studies. The recent advances in CT and MRI and in medical image processing and analysis have given further impetus to pulmonary functional imaging and provide the basis for future expansion of its use in clinical applications. In documenting the utility of state-of-the-art pulmonary functional imaging in diagnostic radiology and pulmonary medicine, this book will be of high value for chest radiologists, pulmonologists, pulmonary surgeons, and radiation technologists.
Structural And Functional Assessments Of Copd Populations Via Image Registration And Unsupervised Machine Learning
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Author : Babak Haghighi
language : en
Publisher:
Release Date : 2018
Structural And Functional Assessments Of Copd Populations Via Image Registration And Unsupervised Machine Learning written by Babak Haghighi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Cluster analysis categories.
There is notable heterogeneity in clinical presentation of patients with chronic obstructive pulmonary disease (COPD). Classification of COPD is usually based on the severity of airflow limitation (pre- and post- bronchodilator FEV1), which may not sensitively differentiate subpopulations with distinct phenotypes. A recent advance of quantitative medical imaging and data analysis techniques allows for deriving quantitative computed tomography (QCT) imaging-based metrics. These imaging-based metrics can be used to link structural and functional alterations at multiscale levels of human lung. We acquired QCT images of 800 former and current smokers from Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS). A GPU-based symmetric non-rigid image registration method was applied at expiration and inspiration to derived QCT-based imaging metrics at multiscale levels. With these imaging-based variables, we employed a machine learning method (an unsupervised clustering technique (K-means)) to identify imaging-based clusters. Four clusters were identified for both current and former smokers. Four clusters were identified for both current and former smokers with meaningful associations with clinical and biomarker measures. Results demonstrated that QCT imaging-based variables in patients with COPD can derive statistically stable and clinically meaningful clusters. This sub-grouping can help better categorize the disease phenotypes, ultimately leading to a development of an efficient therapy.
Thoracic Image Analysis
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Author : Jens Petersen
language : en
Publisher: Springer Nature
Release Date : 2020-11-03
Thoracic Image Analysis written by Jens Petersen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-03 with Computers categories.
This book constitutes the proceedings of the Second International Workshop on Thoracic Image Analysis, TIA 2020, held in Lima, Peru, in October 2020. Due to COVID-19 pandemic the conference was held virtually. COVID-19 infection has brought a lot of attention to lung imaging and the role of CT imaging in the diagnostic workflow of COVID-19 suspects is an important topic. The 14 full papers presented deal with all aspects of image analysis of thoracic data, including: image acquisition and reconstruction, segmentation, registration, quantification, visualization, validation, population-based modeling, biophysical modeling (computational anatomy), deep learning, image analysis in small animals, outcome-based research and novel infectious disease applications.
Imaging And Functional Imaging Of The Lung
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Author : Joseph M. Reinhardt
language : en
Publisher: Frontiers Media SA
Release Date : 2024-04-16
Imaging And Functional Imaging Of The Lung written by Joseph M. Reinhardt and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-16 with Science categories.
Imaging techniques have been used for decades to detect anomalies, study organ function and for diagnostic purposes. Advances in imaging techniques and image processing as well as a wider availability of lung imaging is providing an increasing amount of data and new insights into lung structure and function and their alterations in common lung diseases. Functional imaging biomarkers have the potential to better characterize individual patient phenotypes, predict disease trajectories, and help personalize therapy. The wealth of new data also confronts us with new challenges in terms of identifying, quantifying, deciphering, and standardizing image-based parameters pertaining to regional lung function.
Structural And Functional Assessments Of Longitudinal Copd Subpopulations Via Unsupervised Machine Learning
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Author : Chunrui Zou (PhD)
language : en
Publisher:
Release Date : 2021
Structural And Functional Assessments Of Longitudinal Copd Subpopulations Via Unsupervised Machine Learning written by Chunrui Zou (PhD) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Graphics processing units categories.
Chronic obstructive pulmonary disease (COPD) is highly heterogeneous in terms of syndromes and has tremendous variability in the rate of lung function decline among COPD patients. The SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS) and the Multi-Ethnic Study of Atherosclerosis (MESA) Lung study were established to better understand this complex disease. The objective is to derive clinically meaningful progression sub-groups (clusters) using imaging-based variables extracted from two computed tomography (CT) scans acquired at total lung capacity (TLC) and residual volume (RV) for the two visits of baseline and one-year follow-up, and to understand the effect of smoking status on relevant variables and progression of COPD. We selected 899 subjects from SPIROMICS including 472 former smokers, 372 current smokers and 55 never smokers with a baseline visit and a one-year follow-up visit. A total of 150 quantitative computed tomography (qCT) imaging-based variables, comprising 75 variables at baseline and 75 corresponding progression rates over one year, were derived from the respective inspiration and expiration scans of the two visits. Principal component analysis (PCA) and unsupervised machine learning method (K-means method) were then employed to identify 4 clusters in former smokers and current smokers, respectively. Results showed that we could identify four stable clusters with distinct baseline and progression patterns for former and current smokers, respectively. The clusters were then associated with subject demography, clinical variables, and biomarkers. The longitudinal clusters were shown to be associated with unique progression patterns and clinical characteristics. And the results showed smoking status may affect baseline variables and resulted in different progression patterns between current smokers and former smokers. Compared with cross-sectional clusters which only utilized imaging-based variables at baseline, combination of baseline variables and their progression rates enables identification of longitudinal clusters, resulting in a refinement of cross-sectional clusters.
Deep Learning For Medical Image Analysis
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Author : S. Kevin Zhou
language : en
Publisher: Academic Press
Release Date : 2017-01-18
Deep Learning For Medical Image Analysis written by S. Kevin Zhou and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-18 with Computers categories.
Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache
Deep Learning In Healthcare
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Author : Yen-Wei Chen
language : en
Publisher: Springer Nature
Release Date : 2019-11-18
Deep Learning In Healthcare written by Yen-Wei Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-18 with Technology & Engineering categories.
This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data. Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.
Machine And Deep Learning In Oncology Medical Physics And Radiology
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Author : Issam El Naqa
language : en
Publisher: Springer Nature
Release Date : 2022-02-02
Machine And Deep Learning In Oncology Medical Physics And Radiology written by Issam El Naqa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with Science categories.
This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Deep Learning In Biomedical And Health Informatics
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Author : M. A. Jabbar
language : en
Publisher: CRC Press
Release Date : 2021-09-27
Deep Learning In Biomedical And Health Informatics written by M. A. Jabbar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-27 with Computers categories.
This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques. In short, the volume : Discusses the relationship between AI and healthcare, and how AI is changing the health care industry. Considers uses of deep learning in diagnosis and prediction of disease spread. Presents a comprehensive review of research applying deep learning in health informatics across multiple fields. Highlights challenges in applying deep learning in the field. Promotes research in ddeep llearning application in understanding the biomedical process. Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India. Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA. Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey. Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal. Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.