An accurate segmentation of the pancreas on CT is crucial to identify pancreatic pathologies and extract imaging-based biomarkers. However, prior research on pancreas segmentation has primarily focused on modifying the segmentation model architecture or utilizing pre- and post-processing techniques. In this article, we investigate the utility of anatomical priors to enhance the segmentation performance of the pancreas. Two 3D full-resolution nnU-Net models were trained, one with 8 refined labels from the public PANORAMA dataset, and another that combined them with labels derived from the public TotalSegmentator (TS) tool. The addition of anatomical priors resulted in a 6\% increase in Dice score () and a 36.5 mm decrease in Hausdorff distance for pancreas segmentation (). Moreover, the pancreas was always detected when anatomy priors were used, whereas there were 8 instances of failed detections without their use. The use of anatomy priors shows promise for pancreas segmentation and subsequent derivation of imaging biomarkers.
View on arXiv@article{prasad2025_2504.06921,
title={ Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT },
author={ Anisa V. Prasad and Tejas Sudharshan Mathai and Pritam Mukherjee and Jianfei Liu and Ronald M. Summers },
journal={arXiv preprint arXiv:2504.06921},
year={ 2025 }
}