| Description |
1 online resource (185 p.) |
| Series |
Imaging in Medical Diagnosis and Therapy Series |
|
Imaging in Medical Diagnosis and Therapy Series
|
| Contents |
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Authors -- Contributors -- 1. AI Applications in Radiation Therapy and Medical Physics -- 1.1 Why Artificial Intelligence for Radiotherapy? -- 1.1.1 Automation Applications of AI in Radiotherapy -- 1.2 AI in Radiotherapy -- 1.3 Sample Applications of AI in Radiotherapy -- 1.3.1 AI for Radiotherapy Automation -- 1.3.1.1 Autocontouring (Segmentation) -- 1.3.1.2 Knowledge-Based Planning -- 1.3.1.3 Quality Assurance and Error Detection -- 1.3.2 AI for Radiotherapy Predictive Analytics |
|
1.3.2.1 Outcome Modelling -- 1.3.2.2 Knowledge-Based Adaptive Radiotherapy -- 1.4 Challenges for AI in Radiotherapy and Recommendations -- 1.4.1 Dataset Requirements -- 1.4.2 Validation of AI Models -- 1.4.3 Interpretability and Explainability -- 1.4.4 Quality Assurance -- 1.5 Conclusions -- Acknowledgements -- References -- 2. Machine Learning for Image-Based Radiotherapy Outcome Prediction -- 2.1 Introduction -- 2.2 Imagining Modalities -- 2.3 Quantitative Image Biomarkers -- 2.3.1 Image Standardization -- 2.3.2 Image Delineation -- 2.3.3 Biomarker Extraction -- 2.3.4 Biomarker Repeatability |
|
2.4 RT Outcome Prediction Methodology -- 2.4.1 Biomarker Selection -- 2.4.2 Machine Learning Methodology for Image-Based RT Outcome Prediction -- 2.4.3 Predictor Explainability -- 2.4.4 Predictor Training Design -- 2.5 RT Outcomes of Interest Targeted by Machine Learning Predictors -- 2.5.1 Toxicities -- 2.5.2 Complete Pathological Response -- 2.5.3 Cancer Recurrence -- 2.5.4 Survival -- 2.6 Discussion -- 2.7 Conclusion -- Acknowledgements -- References -- 3. Metric Predictions for Machine and Patient-Specific Quality Assurance -- 3.1 Introduction |
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3.2 Machine Learning Applications to Quality Assurance -- 3.2.1 Automated Chart Review -- 3.2.2 Treatment Delivery Systems -- 3.2.3 Proton QA -- 3.2.4 Patient-Specific QA -- 3.3 Future Directions -- Conflict of Interest -- References -- 4. Data-Driven Treatment Planning, Plan QA, and Fast Dose Calculation -- 4.1 Radiation Therapy and Treatment Planning -- 4.2 Anatomy-Based Dose Distribution Prediction -- 4.3 Dose Distribution Prediction Based on Prior Geometric, Anatomic, and Dosimetric Properties of the Patients |
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4.4 Prediction of Machine Delivery Parameters or Fluence Maps for Treatment Planning -- 4.5 Adjusting Treatment Planning Parameters Directly -- 4.6 Data-Driven Treatment Plan QA -- 4.7 Data-Driven Dose Calculation -- 4.8 Summary -- Acknowledgements -- References -- 5. Reinforcement Learning for Radiation Therapy Planning and Image Processing -- 5.1 Introduction and Overview -- 5.2 Status -- 5.2.1 DRL Application in Treatment Planning for RT -- 5.2.2 DRL Applications in Medical Image Processing for RT -- 5.3 Current and Future Challenges -- 5.4 Future Perspective -- References |
| Notes |
Description based upon print version of record |
|
6. Image Registration and Segmentation |
| Form |
Electronic book
|
| Author |
Xing, Lei
|
| ISBN |
9781000903812 |
|
1000903818 |
|
