HSIEN-CHUN TSENG2026-10-162026-10-162020-01-01Pure ID: 15341090Pure UUID: b08260d1-925f-43c9-9052-c72a1fed01e210.1007/978-3-030-30636-6_58https://www.scopus.com/pages/publications/85075809251https://scholars.csmu.edu.tw/handle/123456789/28182Delineating organs for a long time may cause exhaustion to radiologist’s eyes and mental health, it could lead to results that show different sizes of organs with therapeutic target volume. In this work, we expand on the idea of automatically delineating the organs in Computed Tomography (CT) images of head and neck through the generative adversarial network, which is a deep learning algorithm. In image preprocessing, we generate a bitmap (BMP) image by the combination of CT image and RT structure (RS) file and input it to generator network, which will improve the color and texture quality, last generate a fake Radiation Therapy (RT) image. Finally, the discriminator network takes the fake RT image as an example to compare with the original RT image. To build the predictive model, we continuously train this model to let it learn the rules of delineating organs in CT image, generating more and more images that are similar to the original samples. The approach that proposed in this paper is actually well applied in medicine, and the results of testing are similar to the selected organs or therapeutic targets’ volume that was delineated by the radiologist. We can see that it not only effectively reduces the false positive rate but also promises in applying to other related images.8enjournal article