Pancreatic cancer continues showing poor prognosis with a 5-year overall survival (OS) of 9% [1]. Stereotactic bodyradiotherapy (SBRT) has been increasingly adopted as the treatment option for locally advanced pancreatic cancer(LAPC). Accurate and robust segmentation of the abdominal organs on CT is essential to minimize excessive doses toorgans-at-risk (OARs) such as stomach and duodenum. However, this task is tedious and time-consuming. In this work,we aimed to develop a 3D deep attention U-Net based network to automatically segment the pancreatic SBRT OARs thatcan significantly expedite the treatment planning process, while maintain high segmentation accuracy comparable to theones manually contoured by the experienced physicians. 30 patients previously treated with pancreatic SBRT wereincluded. Their CT and OAR contours including small bowel, large bowel, liver, stomach, spinal cord, left kidney, rightkidney and duodenum were used as the training dataset. Attention gates (AGs) were incorporated in the U-net basednetwork to effectively differentiate the organ boundaries. The mean Dice similarity coefficient (DSC) for large bowel,small bowel, duodenum, left kidney, right kidney, liver, spinal cord, and stomach were 0.89±0.05, 0.86±0.04,0.79±0.04,0.86±0.04, 0.87±0.06, 0.86±0.02, 0.75±0.04 and 0.88±0.06, respectively.
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