IEEE Transactions on Radiation and Plasma Medical Sciences · 2021 · 62 citations · 236 references
MedicineOncologyRadiation OncologyBiomedical ImagingClinical Radiation OncologyTreatment PlanningExternal Beam RadiotherapyImage-guided RadiotherapyImage-guided TherapyRadiation TherapyRadiation Treatment PlanningTreatment Planning Physics3D Image SegmentationAi-based PracticeAutomated Treatment PlanningClinical Workflow ImprovementComputerized Treatment PlanningIntelligent Process AutomationMedical Image RegistrationRadiotherapy Dose Planning
Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.
236
Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, Trevor Darrell · 2015 · 36.9K citations
Focal Loss for Dense Object Detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick et al. · 2017 · 25.7K citations
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
Jun-Yan Zhu, Taesung Park, Phillip Isola et al. · 2017 · 21.9K citations · Full text