Auto-Segmentation for Radiation Oncology : State of the Art (Hardcover)

Chapter 1. Introduction of auto-segmentation in radiation oncology. Jinzhong Yang, Gregory C. Sharp, Mark J. Gooding Chapter 2.Introduction to multi-atlas auto-segmentation. Gregory C. Sharp Chapter 3.Evaluation of atlas selection: How close are we to optimal? Mark J. Gooding Chapter 4. Deformable registration choices for multi-atlas segmentation. Gregory C. Sharp Chapter 5. Evaluation of a multi-atlas segmentation system. Raymond Fang, Laurence Court, and Jinzhong Yang Chapter 6. Introduction to deep learning-based auto-contouring for radiotherapy. Mark J. Gooding Chapter 7. Deep Learning Architecture Design for Multi-organ Segmentation. Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu and Xiaofeng Yang Chapter 8. Comparison of 2D and 3D UNets for Organ Segmentation. Dongdong Gu, Zhong Xue Chapter 9. Organ specific segmentation versus multi-class segmentation using UNet. Xue Feng, Quan Chen Chapter 10. Effect of loss functions in deep learning-based segmentation. Evan Porter, David Solis, Payton Bruckmeier, Zaid A. Siddiqui, Leonid Zamdborg, Thomas Guerrero Chapter 11. Data augmentation for training deep neural networks . Zhao Peng, Jieping Zhou, Xi Fang, Pingkun Yan, Hongming Shan, Ge Wang, X. George Xu, Xi Pei Chapter 12. Identifying possible scenarios where a deep learning auto-segmentation model could fail. Carlos E. Cardenas Chapter 13. Clinical commissioning guidelines. Harini Veeraraghavan Chapter 14. Data curation challenges for artificial intelligence. Ken Chang, Mishka Gidwani, Jay B. Patel, Matthew D. Li, Jayashree Kalpathy-Cramer Chapter 15. On the evaluation of auto-contouring in radiotherapy Mark J. Gooding