This research presents our approaches to the Kaggle UW-Madison GI Tract Image Segmentation Challenge. Radiation oncologists aim to emit X-ray beams targeted at the tumor while simultaneously avoiding the stomach and intestines. Oncologists can see where the tumor is located and administer a precise dose following the presence of tumor cells, which can change daily, thanks to more recent technologies like MR-Linacs. Currently, the task is to outline the location of the gastrointestinal tract (intestines and stomach) to change the direction of the X-ray beam such that the dose is sent to the tumor without touching the organs. This task can take from a few minutes to hours of treatment time unless there is an automated segmentation method that can automate the segmentation process. This research investigates an automated segmentation method based on deep learning that will quicken the segmentation process and eventually help more patients to benefit from effective treatments.