Handbook of Medical Image Computing and Computer Assisted Intervention: The MICCAI Society book Series
Editat de S. Kevin Zhou, Daniel Rueckert, Gabor Fichtingeren Limba Engleză Hardback – 19 oct 2019
- Presents the key research challenges in medical image computing and computer-assisted intervention
- Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society
- Contains state-of-the-art technical approaches to key challenges
- Demonstrates proven algorithms for a whole range of essential medical imaging applications
- Includes source codes for use in a plug-and-play manner
- Embraces future directions in the fields of medical image computing and computer-assisted intervention
Din seria The MICCAI Society book Series
- 20% Preț: 652.00 lei
- 24% Preț: 544.04 lei
- 27% Preț: 598.21 lei
- 25% Preț: 772.12 lei
- 33% Preț: 536.42 lei
- 36% Preț: 534.03 lei
- 37% Preț: 696.72 lei
- 5% Preț: 955.33 lei
- 20% Preț: 716.75 lei
- 32% Preț: 639.18 lei
- 37% Preț: 540.65 lei
- 32% Preț: 738.08 lei
- 31% Preț: 535.98 lei
- 28% Preț: 548.47 lei
- 19% Preț: 676.84 lei
- 33% Preț: 593.68 lei
Preț: 978.94 lei
Preț vechi: 1541.20 lei
-36% Nou
Puncte Express: 1468
Preț estimativ în valută:
187.34€ • 194.40$ • 156.59£
187.34€ • 194.40$ • 156.59£
Carte tipărită la comandă
Livrare economică 10-24 martie
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9780128161760
ISBN-10: 0128161760
Pagini: 1072
Dimensiuni: 191 x 235 x 52 mm
Greutate: 2.03 kg
Editura: ELSEVIER SCIENCE
Seria The MICCAI Society book Series
ISBN-10: 0128161760
Pagini: 1072
Dimensiuni: 191 x 235 x 52 mm
Greutate: 2.03 kg
Editura: ELSEVIER SCIENCE
Seria The MICCAI Society book Series
Public țintă
Researchers, graduate students and practitioners in medical imaging, computer assisted intervention, computer vision and biomedical engineering.Cuprins
1. Image synthesis and superresolution in medical imaging
Jerry L. Prince, Aaron Carass, Can Zhao, Blake E. Dewey, Snehashis Roy, Dzung L. Pham
2. Machine learning for image reconstruction
Kerstin Hammernik, Florian Knoll
3. Liver lesion detection in CT using deep learning techniques
Avi Ben-Cohen, Hayit Greenspan
4. CAD in lung
Kensaku Mori
5. Text mining and deep learning for disease classification
Yifan Peng, Zizhao Zhang, Xiaosong Wang, Lin Yang, Le Lu
6. Multiatlas segmentation
Bennett A. Landman, Ilwoo Lyu, Yuankai Huo, Andrew J. Asman
7. Segmentation using adversarial image-to-image networks
Dong Yang, Tao Xiong, Daguang Xu, S. Kevin Zhou
8. Multimodal medical volumes translation and segmentation with generative adversarial network
Zizhao Zhang, Lin Yang, Yefeng Zheng
9. Landmark detection and multiorgan segmentation: Representations and supervised approaches
S. Kevin Zhou, Zhoubing Xu
10. Deep multilevel contextual networks for biomedical image segmentation
Hao Chen, Qi Dou, Xiaojuan Qi, Jie-Zhi Cheng, Pheng-Ann Heng
11. LOGISMOS-JEI: Segmentation using optimal graph search and just-enough interaction
Honghai Zhang, Kyungmoo Lee, Zhi Chen, Satyananda Kashyap, Milan Sonka
12. Deformable models, sparsity and learning-based segmentation for cardiac MRI based analytics
Dimitris N. Metaxas, Zhennan Yan
13. Image registration with sliding motion
Mattias P. Heinrich, Bartłomiej W. Papiez˙
14. Image registration using machine and deep learning
Xiaohuan Cao, Jingfan Fan, Pei Dong, Sahar Ahmad, Pew-Thian Yap, Dinggang Shen
15. Imaging biomarkers in Alzheimer’s disease
Carole H. Sudre, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin
16. Machine learning based imaging biomarkers in large scale population studies: A neuroimaging perspective
Guray Erus, Mohamad Habes, Christos Davatzikos
17. Imaging biomarkers for cardiovascular diseases
Avan Suinesiaputra, Kathleen Gilbert, Beau Pontre, Alistair A. Young
18. Radiomics
Martijn P.A. Starmans, Sebastian R. van der Voort, Jose M. Castillo Tovar, Jifke F. Veenland, Stefan Klein, Wiro J. Niessen
19. Random forests in medical image computing
Ender Konukoglu, Ben Glocker
20. Convolutional neural networks
Jonas Teuwen, Nikita Moriakov
21. Deep learning: RNNs and LSTM
Robert DiPietro, Gregory D. Hager
22. Deep multiple instance learning for digital histopathology
Maximilian Ilse, Jakub M. Tomczak, Max Welling
23. Deep learning: Generative adversarial networks and adversarial methods
Jelmer M. Wolterink, Konstantinos Kamnitsas, Christian Ledig, Ivana Išgum
24. Linear statistical shape models and landmark location
T.F. Cootes
25. Computer-integrated interventional medicine: A 30 year perspective
Russell H. Taylor
26. Technology and applications in interventional imaging: 2D X-ray radiography/fluoroscopy and 3D cone-beam CT
Sebastian Schafer, Jeffrey H. Siewerdsen
27. Interventional imaging: MR
Eva Rothgang, William S. Anderson, Elodie Breton, Afshin Gangi, Julien Garnon, Bennet Hensen, Brendan F. Judy, Urte Kägebein, Frank K. Wacker
28. Interventional imaging: Ultrasound
Ilker Hacihaliloglu, Elvis C.S. Chen, Parvin Mousavi, Purang Abolmaesumi, Emad Boctor, Cristian A. Linte
29. Interventional imaging: Vision
Stefanie Speidel, Sebastian Bodenstedt, Francisco Vasconcelos, Danail Stoyanov
30. Interventional imaging: Biophotonics
Daniel S. Elson
31. External tracking devices and tracked tool calibration
Elvis C.S. Chen, Andras Lasso, Gabor Fichtinger
32. Image-based surgery planning
Caroline Essert, Leo Joskowicz
33. Human–machine interfaces for medical imaging and clinical interventions
Roy Eagleson, Sandrine de Ribaupierre
34. Robotic interventions
Sang-Eun Song
35. System integration
Andras Lasso, Peter Kazanzides
36. Clinical translation
Aaron Fenster
37. Interventional procedures training
Tamas Ungi, Matthew Holden, Boris Zevin, Gabor Fichtinger
38. Surgical data science
Gregory D. Hager, Lena Maier-Hein, S. Swaroop Vedula
39. Computational biomechanics for medical image analysis
Adam Wittek, Karol Miller
40. Challenges in Computer Assisted Interventions
P. Stefan, J. Traub, C. Hennersperger, M. Esposito, N. Navab
Jerry L. Prince, Aaron Carass, Can Zhao, Blake E. Dewey, Snehashis Roy, Dzung L. Pham
2. Machine learning for image reconstruction
Kerstin Hammernik, Florian Knoll
3. Liver lesion detection in CT using deep learning techniques
Avi Ben-Cohen, Hayit Greenspan
4. CAD in lung
Kensaku Mori
5. Text mining and deep learning for disease classification
Yifan Peng, Zizhao Zhang, Xiaosong Wang, Lin Yang, Le Lu
6. Multiatlas segmentation
Bennett A. Landman, Ilwoo Lyu, Yuankai Huo, Andrew J. Asman
7. Segmentation using adversarial image-to-image networks
Dong Yang, Tao Xiong, Daguang Xu, S. Kevin Zhou
8. Multimodal medical volumes translation and segmentation with generative adversarial network
Zizhao Zhang, Lin Yang, Yefeng Zheng
9. Landmark detection and multiorgan segmentation: Representations and supervised approaches
S. Kevin Zhou, Zhoubing Xu
10. Deep multilevel contextual networks for biomedical image segmentation
Hao Chen, Qi Dou, Xiaojuan Qi, Jie-Zhi Cheng, Pheng-Ann Heng
11. LOGISMOS-JEI: Segmentation using optimal graph search and just-enough interaction
Honghai Zhang, Kyungmoo Lee, Zhi Chen, Satyananda Kashyap, Milan Sonka
12. Deformable models, sparsity and learning-based segmentation for cardiac MRI based analytics
Dimitris N. Metaxas, Zhennan Yan
13. Image registration with sliding motion
Mattias P. Heinrich, Bartłomiej W. Papiez˙
14. Image registration using machine and deep learning
Xiaohuan Cao, Jingfan Fan, Pei Dong, Sahar Ahmad, Pew-Thian Yap, Dinggang Shen
15. Imaging biomarkers in Alzheimer’s disease
Carole H. Sudre, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin
16. Machine learning based imaging biomarkers in large scale population studies: A neuroimaging perspective
Guray Erus, Mohamad Habes, Christos Davatzikos
17. Imaging biomarkers for cardiovascular diseases
Avan Suinesiaputra, Kathleen Gilbert, Beau Pontre, Alistair A. Young
18. Radiomics
Martijn P.A. Starmans, Sebastian R. van der Voort, Jose M. Castillo Tovar, Jifke F. Veenland, Stefan Klein, Wiro J. Niessen
19. Random forests in medical image computing
Ender Konukoglu, Ben Glocker
20. Convolutional neural networks
Jonas Teuwen, Nikita Moriakov
21. Deep learning: RNNs and LSTM
Robert DiPietro, Gregory D. Hager
22. Deep multiple instance learning for digital histopathology
Maximilian Ilse, Jakub M. Tomczak, Max Welling
23. Deep learning: Generative adversarial networks and adversarial methods
Jelmer M. Wolterink, Konstantinos Kamnitsas, Christian Ledig, Ivana Išgum
24. Linear statistical shape models and landmark location
T.F. Cootes
25. Computer-integrated interventional medicine: A 30 year perspective
Russell H. Taylor
26. Technology and applications in interventional imaging: 2D X-ray radiography/fluoroscopy and 3D cone-beam CT
Sebastian Schafer, Jeffrey H. Siewerdsen
27. Interventional imaging: MR
Eva Rothgang, William S. Anderson, Elodie Breton, Afshin Gangi, Julien Garnon, Bennet Hensen, Brendan F. Judy, Urte Kägebein, Frank K. Wacker
28. Interventional imaging: Ultrasound
Ilker Hacihaliloglu, Elvis C.S. Chen, Parvin Mousavi, Purang Abolmaesumi, Emad Boctor, Cristian A. Linte
29. Interventional imaging: Vision
Stefanie Speidel, Sebastian Bodenstedt, Francisco Vasconcelos, Danail Stoyanov
30. Interventional imaging: Biophotonics
Daniel S. Elson
31. External tracking devices and tracked tool calibration
Elvis C.S. Chen, Andras Lasso, Gabor Fichtinger
32. Image-based surgery planning
Caroline Essert, Leo Joskowicz
33. Human–machine interfaces for medical imaging and clinical interventions
Roy Eagleson, Sandrine de Ribaupierre
34. Robotic interventions
Sang-Eun Song
35. System integration
Andras Lasso, Peter Kazanzides
36. Clinical translation
Aaron Fenster
37. Interventional procedures training
Tamas Ungi, Matthew Holden, Boris Zevin, Gabor Fichtinger
38. Surgical data science
Gregory D. Hager, Lena Maier-Hein, S. Swaroop Vedula
39. Computational biomechanics for medical image analysis
Adam Wittek, Karol Miller
40. Challenges in Computer Assisted Interventions
P. Stefan, J. Traub, C. Hennersperger, M. Esposito, N. Navab