Related Experiment Video
Updated: Sep 17, 2025
Robot Assisted Distal Pancreatectomy with Celiac Axis Resection DP-CAR for Pancreatic Cancer: Surgical Planning and Technique
Published on: August 14, 2021
3D auto-segmentation of pancreas cancer and surrounding anatomical structures for surgical planning
Jinsoo Rhu1, Namkee Oh1, Gyu-Seong Choi1
1Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Background:
This multicenter study aimed to develop a deep learning-based autosegmentation model for pancreatic cancer and surrounding anatomical structures using computed tomography (CT) to enhance surgical planning.
Materials And Methods:
We included patients with pancreatic cancer who underwent pancreatic surgery at three tertiary referral hospitals. A hierarchical Swin Transformer V2 model was implemented to segment the pancreas, pancreatic cancers, and peripancreatic structures from preoperative contrast-enhanced CT scans. Data were divided into training and internal validation sets at a 3:1 ratio (from one tertiary institution), with separately prepared external validation set (from two separate institutions). Segmentation performance was quantitatively assessed using the dice similarity coefficient (DSC) and qualitatively evaluated (complete vs. partial vs. absent).
Results:
A total of 275 patients (51.6% male, mean age 65.8 ± 9.5 years) were included (176 training group, 59 internal validation group, and 40 external validation group). No significant differences in baseline characteristics were observed between the groups. The model achieved an overall mean DSC of 75.4 ± 6.0 and 75.6 ± 4.8 in the internal and external validation groups, respectively. It showed high accuracy particularly in the pancreas parenchyma (84.8 ± 5.3 and 86.1 ± 4.1) and lower accuracy in pancreatic cancer (57.0 ± 28.7 and 54.5 ± 23.5). The DSC scores for pancreatic cancer tended to increase with larger tumor sizes. Moreover, the qualitative assessments revealed high accuracy in the superior mesenteric artery (complete segmentation, 87.5-100%), portal and superior mesenteric vein (97.5-100%), pancreas parenchyma (83.1-87.5%), but lower accuracy in cancers (62.7-65.0%).
Conclusion:
The deep learning-based autosegmentation model for 3D visualization of pancreatic cancer and peripancreatic structures showed robust performance. Further improvement will enhance many promising applications in clinical research.
