Author(s) :

Xiaochuan Liang.

Volume/Issue :

Abstract :

Radiation therapy is widely used to treat cancerous cells; however, it is costly and has significant side effects if not properly used. During treatment, some organs, such as the liver, may shift slightly due to the patient’s respiration, making accurate real-time tracking and contouring essential to minimize damage to healthy tissues. Current tracking systems usually include precise real-time positioning and contouring of the treatment area, which require costly imaging technologies to run throughout the entire therapy session, increasing the overall treatment cost and limiting the availability of advanced equipment like MRIs to hospitals. This research studies the application of Pix2Pix, a Conditional Generative Adversarial Network (GAN), as an alternative to traditional contouring methods in liver cancer radiotherapy. The result shows that a well-trained Pix2Pix can help accurately track liver movement during respiration. With AI computing power becoming increasingly affordable, I anticipate that GANs like Pix2Pix can be industrialized to make cancer treatment more accessible and cost-effective.