Deep Learning Segmentation in Pancreatic Ductal Adenocarcinoma Imaging

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Purpose: Accurately quantifying the extent vessel coverage in the pancreas is essential for determining the feasibility of surgery. The aim of this study is to train a segmentation model specialized for Pancreatic Ductal Adenocarcinoma (PDAC) imaging, focusing on delineating pancreas, tumor, arteries (celiac artery, superior mesenteric artery, common hepatic artery), and veins (portal vein, superior mesenteric vein) using an improved Attention Unet CNN approach.Methods: Data from 100 PDAC patients treated at the Ruijin Hospital between 2020 and 2022 were utilized. Using Synapse 3D software, masks of the tumor, arteries (including the celiac artery, superior mesenteric artery, and hepatic artery), and veins (including the superior mesenteric vein and portal vein) were generated semi-automatically and reviewed by radiologists. Standard image processing techniques, including adjustment of window level to 60 and width to 350 and histogram equalization were subsequently applied. Two types of CNN-based Attention Unet segmentation models were developed: (1) Unified Unet Model that segments all four components simultaneously, and (2) four Individual Unet Models that segments pancreas, tumor, veins, and arteries separately. The train-validation-test data assignment was set to 7:2:1. The segmentation efficacy was assessed using Dice similarity coefficient, with the Adam optimizer utilized for optimization. Results: The individual segmentation models achieve notable performance: pancreas (Accuracy: 0.84, IoU: 0.81, Dice: 0.76), tumor (Accuracy: 0.78, IoU: 0.77, Dice: 0.68), vein (Accuracy: 0.88, IoU: 0.86, Dice: 0.80), and artery (Accuracy: 0.91, IoU: 0.93, Dice: 0.93). However, the unified model demonstrates inferior performance with accuracy, IoU, and Dice coefficient scores of 0.61, 0.50, and 0.45, respectively. Conclusion: Accurate segmentation models have been developed for pancreas, pancreatic tumors, arteries, and veins in PDAC patients. This will enable the efficient quantification of vessel coverage in the pancreas, thereby enhancing the decision-making process regarding the feasibility of surgery for PDAC patients. The findings also demonstrate that Individual Models outperform the Unified Model in segmentation accuracy, highlighting the importance of tailored segmentation strategies for different anatomical structures in PDAC imaging.





Zhang, Haoran (2024). Deep Learning Segmentation in Pancreatic Ductal Adenocarcinoma Imaging. Master's thesis, Duke University. Retrieved from


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