Related Experiment Video
Updated: Mar 11, 2026
Multianimal Magnetic Resonance Imaging for Tumor Measurements in Pancreatic Cancer Mouse Models
Published on: February 3, 2026
Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation
Shuiping Gou1, Percy Lee2, Peng Hu3
1Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shanxi, China; Department of Radiation Oncology, University of California, Los Angeles, USA.
Purpose:
With the advent of MR guided radiotherapy, internal organ motion can be imaged simultaneously during treatment. In this study, we evaluate the feasibility of pancreas MRI segmentation using state-of-the-art segmentation methods.
Methods And Material:
T2 weighted HASTE and T1 weighted VIBE images were acquired on 3 patients and 2 healthy volunteers for a total of 12 imaging volumes. A novel dictionary learning (DL) method was used to segment the pancreas and compared to t mean-shift merging (MSM), distance regularized level set (DRLS), graph cuts (GC) and the segmentation results were compared to manual contours using Dice's index (DI), Hausdorff distance and shift of the-center-of-the-organ (SHIFT).
Results:
All VIBE images were successfully segmented by at least one of the auto-segmentation method with DI >0.83 and SHIFT ≤2 mm using the best automated segmentation method. The automated segmentation error of HASTE images was significantly greater. DL is statistically superior to the other methods in Dice's overlapping index. For the Hausdorff distance and SHIFT measurement, DRLS and DL performed slightly superior to the GC method, and substantially superior to MSM. DL required least human supervision and was faster to compute.
Conclusion:
Our study demonstrated potential feasibility of automated segmentation of the pancreas on MRI images with minimal human supervision at the beginning of imaging acquisition. The achieved accuracy is promising for organ localization.
