Deep learning-based organ-at-risk segmentation, registration and dosimetry on cone beam computed tomography images in radiation therapy: A comprehensive review.

Abstract: Cone-beam computed tomography (CBCT) is pivotal in image-guided radiotherapy (IGRT), yet it faces challenges in accurate organ-at-risk (OAR) segmentation, image registration, and dosimetry. Deep learning, particularly Generative Adversarial Networks (GAN) and Deep Convolutional Neural Networks (DCNN) has shown promise in addressing these challenges. This review explores the latest advancements in deep learning-based methodologies for enhancing CBCT application in radiotherapy. GANs have been employed to generate high-fidelity synthetic CT images, improving the accuracy of OAR segmentation and enabling precise dose calculations. DCNNs, on the other hand, have been instrumental in mitigating artifacts, enhancing image quality, and predicting dose distributions with high precision. Studies demonstrate that these techniques significantly improve the accuracy of OAR delineation and registration, leading to better treatment planning and delivery. Integrating deep learning models with traditional CBCT makes it possible to achieve real-time adaptation to anatomical changes and optimize patient-specific treatment protocols. This review highlights key findings, methodological innovations, and clinical implications, underscoring the transformative potential of deep learning in CBCT-based radiotherapy. The evolution of GANs and DCNNs promises to refine dosimetric accuracy and treatment outcomes further, heralding a new era of precision radiotherapy.