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In treating gastrointestinal cancer using radiotherapy, the role of theradiation oncologist is to administer high doses of radiation, through x-raybeams, toward the tumor while avoiding the stomach and intestines. With theadvent of precise radiation treatment technology such as the MR-Linac,oncologists can visualize the daily positions of the tumors and intestines,which may vary day to day. Before delivering radiation, radio oncologists mustmanually outline the position of the gastrointestinal organs in order todetermine position and direction of the x-ray beam. This is a time consumingand labor intensive process that may substantially prolong a patient'streatment. A deep learning (DL) method can automate and expedite the process.However, many deep neural networks approaches currently in use are black-boxeswhich lack interpretability which render them untrustworthy and impractical ina healthcare setting. To address this, an emergent field of AI known asExplainable AI (XAI) may be incorporated to improve the transparency andviability of a model. This paper proposes a deep learning pipeline thatincorporates XAI to address the challenges of organ segmentation.

Gastro-Intestinal Tract Segmentation Using an Explainable 3D Unet