CT-based pancreatic multi-organ segmentation by a 3D deep attention U-net network

Yingzi Liu; Yang Lei; Yabo Fu; Tonghe Wang; Xiangyang Tang; Sibo Tian; Tian Liu; Walter J. Curran; Pretesh Patel; Xiaofeng Yang

doi:10.1117/12.2550374

2 March 2020 CT-based pancreatic multi-organ segmentation by a 3D deep attention U-net network

Yingzi Liu, Yang Lei, Yabo Fu, Tonghe Wang, Xiangyang Tang, Sibo Tian, Tian Liu, Walter J. Curran, Pretesh Patel, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications; 1131813 (2020) https://doi.org/10.1117/12.2550374
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Pancreatic cancer continues showing poor prognosis with a 5-year overall survival (OS) of 9% [1]. Stereotactic body radiotherapy (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 to organs-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 that can significantly expedite the treatment planning process, while maintain high segmentation accuracy comparable to the ones manually contoured by the experienced physicians. 30 patients previously treated with pancreatic SBRT were included. Their CT and OAR contours including small bowel, large bowel, liver, stomach, spinal cord, left kidney, right kidney and duodenum were used as the training dataset. Attention gates (AGs) were incorporated in the U-net based network 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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Yingzi Liu, Yang Lei, Yabo Fu, Tonghe Wang, Xiangyang Tang, Sibo Tian, Tian Liu, Walter J. Curran, Pretesh Patel, and Xiaofeng Yang "CT-based pancreatic multi-organ segmentation by a 3D deep attention U-net network", Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 1131813 (2 March 2020); https://doi.org/10.1117/12.2550374

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