Handbook of Medical Image Computing and Computer Assisted Intervention

Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. Presents the key research challenges in medical image computing and computer-assisted interventionWritten by leading authorities of the Medical Image Computing and Computer Assisted Intervention Society (MICCAI)Contains state-of-the-art technical approaches to key challengesDemonstrates proven algorithms for a whole range of essential medical imaging applicationsIncludes source codes for use in a plug-and-play mannerEmbraces future directions in the fields of medical image computing and computer-assisted intervention INDICE: 1. Image synthesis, superresolution, denoising 2. Machine learning for image reconstruction 3. CAD for Liver Analysis 4. CAD for lung imaging 5. Mining text and disease 6. Multi-atlas segmentation 7. Segmentation using Adversarial Image-to-image Network 8. Marginal space learning and deep learning 9. Segmentation using Shape Regression Machine 10. Deep Multi-level Contextual Networks for Biomedical Image Segmentation 11. Segmentation using optimal Graph Search and just enough interaction 12. Deformable models with machine learning for segmentation 13. Image registration using free-form deformations 14. Image registration using demons 15. Image registration with sliding motion 16. Image Registration using machine and deep learning 17. LDDMM based registration 18. Imaging Biomarkers for Neurodegenerative Diseases 19. Machine learning based imaging biomarkers in large scale population studies 20. Imaging Biomarkers for Oncology 21. Imaging Biomarkers for CVD 22. Radiomics 23. Random forests 24. Manifold learning 25. Deep learning: CNNs 26. Deep learning: RNNs and LSTM 27. Multiple instance learning 28. Deep learning: GANs, adversarial methods 29. Statistical shape models 30. Overview of CAI 31. Interventional Imaging: X-ray / CT 32. Interventional Imaging: MR 33. Interventional Imaging: Ultrasound 34. Interventional Imaging: Vision 35. Interventional Imaging: Biophotonics 36. External Tracking 37. Treatment Planning 38. Human-Machine Interfaces 39. Interventional Robotics 40. Systems Integration 41. Clinical Translation 42. Intervention training 43. Surgical data science in skill assessment and training 44. Computational Biomechanics 45. Challenges for CAI

• ISBN: 978-0-12-816176-0
• Editorial: Academic Press
• Encuadernacion: Cartoné
• Páginas: 1080
• Fecha Publicación: 01/10/2019
• Nº Volúmenes: 1
• Idioma: Inglés