RTGAN: ORGAN CONTOURS IN RADIATION THERAPY WITH GENERATIVE ADVERSARIAL NETWORK

Abstract

Generally, radiation oncology applies evaluation and prediction in medical imaging and diagnosis, specifically for contouring organs, which results in the production of the clinical target volume (CTV) that corresponds to disease risk and organ exclusion. Medical physicists contour organs and combine computed tomography (CT) scans to digital imaging and communications in medicine (DICOM) radiation therapy (RT) to assist physicians for diagnosing tumors and calculating the dosages in treatments including radiation and chemotherapy. Thus, to generate RT images with high accuracy, this paper proposes a new Generator Adversarial Network (GAN) for RT images called radiation therapy GAN (RTGAN). We combine multiple loss functions with synthetic similarity DICOM-RT images and compare the results with Pinnacle, a radiation oncology treatment planning system. Further, we evaluate the method to get a score of 0.984 in structured similarity (SSIM) and 31.26 in peak signal-to-noise ratio (PSNR) and find that it costs 0.058 s to finish contouring one CT image. The proposed method is applied and tested in the department of radiation oncology at the Chung Shan Medical University Hospital, and the results are similar to the ground truth images. Thus, it not only effectively reduces the false-positive rate but also makes a breakthrough in medicine.