Biomedical information technology:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London, United Kingdom ; San Diego, CA, United States
Academic Press, an imprint of Elsevier
[2020]
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| Ausgabe: | Second edition |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xxii, 798 Seiten Illustrationen |
| ISBN: | 9780128160343 |
Internformat
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Datensatz im Suchindex
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| adam_text | Contents Contributors xiii Acknowledgements xix Introduction xxi Part One: Biomedical data technologies 1. Medical imaging 3 Xiaofeng Zhang, Nadine Smith, and Andrew Webb 1.1 Introduction 1.2 Digital radiography 1.3 Computed tomography 1.4 Nuclear medicine 1.5 Ultrasonic imaging 3 3 7 10 17 1.6 Magnetic resonance imaging 24 1.7 Diffuse optical imaging 32 1.8 Biosignals 39 1.9 Digital cameras and microscopes 41 Appendix 42 Exercises 45 Further reading 48 2. Biomedical sensors 51 Hao Wan, Liujing Zhuang, Yuxiang Pan, Fan Gao, Jiawei Tu, Bin Zhang, and Ping Wang 2.1 Introduction 51 2.2 Wearable devices 52 2.3 Biochip 58 2.4 Biosensors 63 2.5 Implantable sensors 67 2.6 Neural sensing and interfacing 71 2.7 Summary 76 References 76
vi Contents 3. Biological computing 81 Yi Fu, Tsung-Heng Tsai, Chunhong Mao, Seong K. Mun, Habtom W. Ressom, Minkun Wang, Zhen Zhang, and Yue Wang 3.1 Introduction 81 3.2 General workflow for the analysis of biological samples 81 3.3 Overview of genomic methods 82 3.4 Overview of proteomic methods 3.5 Biological databases and open-source 85 software 3.6 Biological network analysis 92 97 3.7 Summary 101 Acknowledgments 101 References 101 4. Picture archiving and communication systems and electronic medical records for the healthcare enterprise 105 Brent J. Liu and H.K. Huang 4.1 Introduction 105 4.2 Picture archiving and communicationsystem infrastructure 107 4.3 Picture archiving and communicationsystem components and workflow 118 4.4 Picture archiving and communication system server andImage archive 125 4.5 Picture archiving and communicationsystem clinical experiences 136 4.6 Introduction to hospital clinical systems 144 4.7 Picture archiving and communicationsystems and electronic medical records 150 4.8 Summary 162 4.9 Exercises 163 Further reading 163 Part Two: Artificial intelligence and big data processing in biomedicine 5. Machine learning in medical imaging 167 Ashnil Kumar, Lei Bi, Jinman Kim, and David Dagan Feng 5.1 Medical imaging 5.2 Machine Intelligence and machine learning 167 168 5.3 Supervised learning 169 5.4 Unsupervised learning 179 5.5 Semlsupervised learning 184 5.6 Reinforcement learning 185 5.7 Summary 187 5.8 Questions 187 References 188
Contents vii 6. Health intelligence 197 Arash Shaban-Nejad, Rishikesan Kamaleswaran, Eun Kyong Shin, and Oguz Akbilgic 6.1 Introduction 6.2 Predictive modeling and forecasting for health intelligence 197 198 6.3 Multiple facets of health intelligence 200 6.4 Conclusions 209 References 209 7. Artificial intelligence in bioinformatics: automated methodology development for protein residue contact map prediction 217 Shi-Hao Feng, Jia-Yan Xu, and Hong-Bin Shen 7.1 Background 217 7.2 Evaluation of prediction performance 220 7.3 Contact map prediction models 220 7.4 Performance significantly depends on MSA features 233 7.5 Conclusions References 8. Deep learning in biomedical image analysis 234 234 239 Minjeong Kim, Chenggang Yan, Defu Yang, Qian Wang, Junbo Ma, and Guorong Wu 8.1 Introduction—deep learning meets medical Image analysis 239 8.2 Basics of deep learning 241 8.3 Applications in biomedical imaging 249 8.4 Conclusion 261 References 261 9. Automatic lesion detection with three-dimensional convolutional neural networks 265 Qi Dou, Hao Chen, Jing Qin, and Pheng-Ann Heng 9.1 Introduction 265 9.2 3-D convolutional neural network 266 9.3 Efficient fully convolutional architecture 269 9.4 Two-stage cascaded framework for detection 273 9.5 Case study I: cerebral microbleed detection Inbrain magnetic resonance Imaging 275 9.6 Case study II: lung nodule detection in chest computed tomography 280 9.7 Discussion 288 9.8 Conclusions 291 Acknowledgments 291 References 291
viii Contents 10. Biomedical image segmentation for precision radiationoncology 295 Hui Cui, Hao Wang, Ke Yan, Xiuying Wang, Wangmeng Zuo, and David Dagan Feng 10.1 Introduaion 295 10.2 Graph models in biomedical image segmentation 296 10.3 305 Deep network in object detection and segmentation 10.4 Applications for medical image processing 313 10.5 Computational delineation and quantitative heterogeneity analysis for personalized radiation treatment planning 314 10.6 Summary 315 References 316 11. Content-based large-scale medical image retrieval 321 Weidong Cai, Yang Song, Ashnil Kumar, Jinman Kim, and David Dagan Feng 11.1 Introduction 11.2 Fundamentals of content-based image retrieval 11.3 Visual feature-based retrieval 321 322 330 11.4 Geometric spatial feature-based retrieval 337 11.5 Clinical contextual and semantic retrieval 343 11.6 Summary 356 11.7 356 Exercises Acknowledgments 357 References 357 12. Diversity and novelty in biomedical information retrieval 369 Xiangdong An, Jimmy Xiangji Huang, and Yuqi Wang 12.1 Introduction and motivation 12.2 Overview of novelty and diversity boosting in biomedical information retrieval 369 370 12.3 Boosting diversity and novelty in biomedical information retrieval 372 12.4 Diversity and novelty evaluation metrics 376 12.5 385 12.6 Evaluation results of diversity and novelty metrics Summary and future work 392 Acknowledgments 393 References 393 13. Toward large-scale histopathúlogical image analysisvia deep learning 397 Bin Kong, Zhongyu Li, and Shaoting Zhang 13.1 Introduction 13.2 Unique challenges in histopathological image
analysis 13.3 Computer-aided diagnosis for histopathological image analysis 397 398 400 13.4 Deep learning for histopathological image analysis 402 13.5 407 High-throughput histopathological image analysis 13.6 Summary 411 References 412
Contents ___ ______ ___ __________________ __ 14. Data modeling and simulation ¡X 415 Alessandra Bertoldo and Claudio Cobelli 14.1 Introduction 14.2 Compartmental models 14.3 Model identification 14.4 Model validation 415 416 422 441 14.5 Case study 442 14.6 Quantification of medical images 445 14.7 458 Exercises References 459 Further reading 460 15. Image-based biomedical data modeling and parametricimaging 461 Lingfeng Wen, Stefan Eberl, Michael J. Fulham, and David Dagan Feng 15.1 Introduction 15.2 Parametric image estimation methods 461 472 15.3 Noninvasive methods 492 15.4 Applications of parametric imaging and kinetic modeling 498 15.5 512 Summary References 16. Molecular imaging in biology and pharmacology 512 523 Sung-Cheng Huang, Anna M. Wu, and Jorge R. Barrio 16.1 Introduction and background 523 16.2 Considerations for quantitative molecular imaging 528 16.3 534 Design/development of molecular imaging probes 16.4 Molecular imaging of beta-amyloids and neurofibrillary tangles 539 16.5 Molecular imaging using antibody probes 542 16.6 Some other molecular imaging applications 545 16.7 Summary and future perspectives 549 16.8 Exercises 554 References 554 17. Biomedical image visualization and display technologies 561 Jinman Kim, Younhyun Jung, David Dagan Feng, and Michael J. Fulham . 17.1 Introduction 561 17.2 Biomedical Imaging modalities 564 17.3 Biomedical image visualization pipeline 567 17.4 Volume rendering techniques 567
x Contents 17.5 Display technology Development platforms for biomedical image visualization 577 17.7 Conclusions 579 17.8 Questions 579 References 18. 574 17.6 Biomedical image characterization and radiogenomics 579 585 Ming Fan, Yitan Zhu, Lihua Li, Robert Clarke, and Yue Wang 18.1 Introduction 585 18.2 Radiomic characterization of medical imaging 586 18.3 Radiogenomics for uncovering cancer mechanism 595 18.4 Radiomics as signatures for non֊invasive probes of cancer related molecular biomarkers 18.5 Radiogenomic applications in cancer diagnosis and treatment 600 603 18.6 Summary 607 References 607 Part Three: Emerging technologies in biomedicine 19. Medical robotics and computer-integrated interventional medicine 617 Russell H. Taylor, Peter Kazanzides, Gregory S. Fischer, and Nabil Simaan 19.1 Introduction 617 19.2 Technology and techniques 619 19.3 Surgical CAD/CAM 639 19.4 Surgical assistance 645 19.5 19.6 Summary and conclusion Exercises References 20. Virtual and augmented reality in medicine 653 654 655 673 Sheng Bin, Saleha Masood, and Younhyun Jung 20.1 Introduction 20.2 Surgical education with virtual reality technologies 673 674 20.3 Minimally invasive surgery with augmented reality 677 20.4 Mental health care with virtual reality and augmented reality technologies 679 205 Other medical applications with virtual and augmented reality technologies 680 20.6 Future research and development opportunities as well as challenges in the healthcare zone 681 20.7 Summary 683 References 683 Further reading 686
Contents xi 21. Sensory information feedback for neural prostheses 687 Douglas J. Weber, Manzhao Hao, Michael A. Urbin, Caroline Schoenewald, and Ning Lan 21.1 Introduction 687 21.2 Background: anatomy and physiology of the somatosensory system 688 21.3 Overview of sensory feedback in neural prostheses 696 21.4 Anatomical targets and Interface technologies for stimulating somatosensory inputs 698 21.5 Anatomical targets and interface technologies for sensing somatosensory inputs 706 21.6 Summary and future directions 709 References 711 22. Mobile health (m-health): evidence-based progress or scientific retrogression 717 Robert S.H. Istepanian and Turki AlAnzi 22.1 Introduction 717 22.2 The science of mobile health: recent developments and challenges 730 22.3 731 Conclusions References 732 Further reading 733 23. Health and medical behavior informatics 735 Longbing Cao Index 23.1 Introduction 23.2 Behavior and behavior informatics · 735 736 23.3 Health and medical behavior 742 23.4 Health and medical behavior informatics 746 23.5 Related work 755 23.6 Prospects 757 Acknowledgment 759 References 759 763
BIOMEDICAL INFORMATION TECHNOLOGY I HI ПГ» BY DAVID DAGEN FENG SECOND EDITION Information technology is the core technology and driving force leading to the fourth industrial revolution as well as the biomedical and healthcare revolutions. The enormous growth in the field of biotechnology necessitates the utilization of information technology for the management, flow, and organization of data. The field continues to evolve with the development of new applications to fit the needs of biomedicine. From molecular imaging to healthcare knowledge management, the storage, access, and analysis of data contribute significantly to biomedical research and practice. Biomedical Information Technology, Second Edition, contains practical integrated clinical applications for disease detection, diagnosis, surgery, therapy, and biomedical knowledge discovery including the latest advances in the field, such as biomedical sensors, machine intelligence, artificial intelligence, deep learning in medical imaging, neural networks, natural language processing, largescale histopathological image analysis, virtual, augmented, and mixeci reality, Internet of Things , and social media for healthcare, as well as data analytics and behavioral informatics in modern medicine. Tliis is a valuable resource for clinical and biomedical engineering researchers, academics, and healthcare professionals who wish to gain a greater understanding of how data can be efficiently managed and utilized through data compression, modeling, processing, registration, visualization, communication, and large-scale biological
computing. Professor David Dagan Feng is Director, Biomedical Multimedia Information Technology Research Group, at the University of Sydney. In conjunction with his team, he has been responsible for more than 50 key research projects, published over 900 scholarly research papers, pioneered several new research directions, and made a number of landmark contributions in his field. He is a Fellow of ACS, HKIF., 11,1, IF.F.E, and the Australian Academy of Technological Sciences and Engineering.
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