Computer-Aided Oral and Maxillofacial Surgery

Developments, Applications, and Future Perspectives

1st Edition - April 29, 2021
Imprint: Academic Press
Editors: Jan Egger, Xiaojun Chen
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 3 2 9 9 - 6
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 3 4 2 3 - 5

Computer-Aided Oral and Maxillofacial Surgery: Developments, Applications, and Future Perspectives is an ideal resource for biomedical engineers and computer scientists, clinician… Read more

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Computer-Aided Oral and Maxillofacial Surgery: Developments, Applications, and Future Perspectives is an ideal resource for biomedical engineers and computer scientists, clinicians and clinical researchers looking for an understanding on the latest technologies applied to oral and maxillofacial surgery. In facial surgery, computer-aided decisions supplement all kind of treatment stages, from a diagnosis to follow-up examinations. This book gives an in-depth overview of state-of-the-art technologies, such as deep learning, augmented reality, virtual reality and intraoperative navigation, as applied to oral and maxillofacial surgery. It covers applications of facial surgery that are at the interface between medicine and computer science.

Examples include the automatic segmentation and registration of anatomical and pathological structures, like tumors in the facial area, intraoperative navigation in facial surgery and its recent developments and challenges for treatments like zygomatic implant placement.

Cover
Title page
Contents
Copyright
Contributors
About the editors
Preface
1: Medical image segmentation in oral-maxillofacial surgery
Abstract
1. Introduction
Acknowledgment
2: Registration in oral and maxillofacial surgery
Abstract
1. Medical image registration
2. The classification of medical registration methodology
3. The general framework of image registration procedure
4. Image registration in oral and maxillofacial surgery
5. Image-to-patient registration in oral and maxillofacial surgery
6. Software tools for medical image registration
3: Deep learning and generative adversarial networks in oral and maxillofacial surgery
Abstract
1. Introduction
2. Deep learning and its applications
3. Generative adversarial networks and deep perceptual losses
4. Uncertainty quantification in deep learning
5. Outline and future directions
6. Conclusion
Acknowledgment
4: Radiomics in head and neck cancer
Abstract
1. Introduction
2. Head and neck cancer
3. Radiomics: the concept and workflow
4. Radiomics in head and neck cancer—literature review
5. Conclusion
Acknowledgments
5: Augmented reality in oral and maxillofacial surgery
Abstract
1. Introduction
2. Augmented reality: concepts and technology
3. Augmented reality in medicine: current practice and challenges
4. Augmented reality in oral and cranio-maxillofacial surgery
Acknowledgments
6: Haptics and virtual reality for oral and maxillofacial surgery
Abstract
1. Introduction
2. Haptics in virtual surgery
3. Surgical simulation for the oral and maxillofacial surgery
3.4. Haptic rendering method
7: Surgical navigation
Abstract
1. From stereotaxy to surgical navigation
2. Working principle of surgical navigation
3. Technical details of surgical navigation used in OCMF surgery
4. Clinical application of surgical navigation in OCMF surgery
5. New techniques used in OCMF surgical navigation
8: 3D printing and 3D printed scaffolds
Abstract
1. Introduction of 3D printing
2. Methods of 3D printing
3. Introduction of 3D printed scaffolds
4. 3D-printed scaffolds used in bone reconstruction
5. Summary
9: Clinical practice (Graz, Austria and Gießen, Germany)
Abstract
1. Clinical centers in Austria
2. Clinical imaging modalities used for computer-aided oral and maxillofacial surgery
3. Use and practice of clinical computer-aided methods and technologies
4. Limitations of computer-aided technologies and procedures
5. Future perspectives
Acknowledgment
10: Real-time navigation system in implant dentistry
Abstract
1. The history of clinical application of dynamic navigation system
2. Comparison of static surgical guides and dynamic navigation systems
3. Dynamic navigation system in conventional implant surgery
4. Real-time navigation system in zygomatic implant surgery
5. Workflow for real-time navigation system
6. Clinical outcome of real-time navigation system
7. Advantages and disadvantages of a real-time navigation system in zygomatic implant surgery
8. Major error factors of dynamic navigation system
9. Application of the dynamic navigation system in teaching and its learning curve
10. Current limitations of dynamic navigation systems
11. Modifications and further developments of real-time navigation systems
Index

Jan Egger

Jan Egger has over 10 years of experience in developing algorithms in medical image analysis and computer vision, including 5 years of postdoctoral research and development in the healthcare industry. He has extensive experience in implementing algorithms and software in the field of surgical planning, and published over 100 peer-reviewed papers and several patents. Jan holds a Ph.D. and a German Habilitation in Computer Science, and an interdisciplinary Ph.D. in Human Biology. The interdisciplinary Ph.D. was obtained when working in the Neurosurgery Department of the University Hospital in Marburg, Germany (Chairman Professor Dr. Nimsky), where he developed several algorithms for brain tumors, aneurysms and deep brain stimulation, which can be used within the Operation Microscope. He is the initiator of Studierfenster (studierfenster.at), an online platform for medical image analysis. His main research interests are Translational Science in Medical Image Analysis and Image-Guided Therapy. Jan has currently a dual appointment and is employed 50% at the Graz University of Technology and 50% at the Medical University of Graz.

Affiliations and expertise

Visiting Professor, Shanghai Jiao Tong University; Privatdozent, Graz University of Technology, Austria

Xiaojun Chen

Prof. Xiaojun Chen is with School of Mechanical Engineering, Shanghai Jiao Tong University (SJTU), China. He received his Ph. D from SJTU in 2006. His research focuses on computer assisted surgery, including biomedical image analysis, image-guided interventions, artificial intelligence in biomedical physics and analysis, VR/AR/MR technology in medicine, medical robotics and biomedical manufacturing. As a visiting professor, he had worked at Harvard Medical School, TIMC-IMAG lab (CNRS, France), the OMFS-IMPATH lab (KU Leuven, Belgium), and the CISTIB lab (the University of Sheffield, UK). He is the author and co-author of more than 200 peer-reviewed articles, and the PI of more than 30 research projects, including seven funded by National Natural Science Foundation of China. He was granted Second Prize of National Science & Technology Progress Award of China (2019). His important academic responsibilities include an associate editor of Computerized Medical Imaging and Graphics, and Computer Assisted Surgery, and a member of the editorial board of Physics in Medicine & Biology, International Journal of Computer Assisted Radiology and Surgery, and Medical Engineering & Physics.

Affiliations and expertise

Professor, Institute of Biomedical Manufacturing, School of Mechanical Engineering, Shanghai Jiao Tong University (SJTU), China

Life Sciences

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