Brain tumor segmentation via generative adversarial network

With the development in technology and the complication of tumors presented in human brains, magnetic resonance imaging (MRI) has been an essential tool that physicians rely on to look at the structure of brain tissue. The ability to distinguish between normal tissue and tumor tissue has become the key to effective medical diagnosis and treatments. This thesis discusses developing machine learning techniques that will effectively segment the different parts of an MR image, namely the whole tumor, the tumor core and the enhancing tumor core. Using the dataset provided by the Perelman School of Medicine’s Center for Biomedical Image Computing & Analytics from University of Pennsylvania, a generative adversarial network (GAN) takes in imperfect predictions from other tumor segmentation networks and outputs more accurate and realistic predictions that resemble the ground truth. After our analysis, we see that the improvements that resulted from using a Pix2Pix GAN are shown in some cases, but the effects are unstable. Therefore, we conclude that fundamental changes in our GAN network are necessary to achieve more accurate results, and further research should be done on exploring the effects of using a Vox2Vox GAN.